subject:(Emotion classification)

University of Manchester

1. Ahmad, Tariq Naseer. CLASSIFICATION OF TWEETS USING MULTIPLE THRESHOLDS WITH SELF-CORRECTION AND WEIGHTED CONDITIONAL PROBABILITIES.

Degree: 2020, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323659

► Emotion analysis aims to recognise emotions such as anger, joy and trust from texts. It is a trending topic because it can be applied in… (more)

▼ Emotion analysis aims to recognise emotions such as anger, joy and trust from texts. It is a trending topic because it can be applied in important areas such as marketing, healthcare and customer services. Current, state-of-the-art, solutions are based around supervised models that are trained using examples that have been manually annotated. This is subjective, expensive and time-consuming. This thesis explores the problem of multi-label emotion classification of tweets. The task is particularly difficult as tweets are notoriously awkward to work with as they are noisy in nature and may contain unstructured text, abbreviations, slang, acronyms, emoticons and incorrect grammar and spelling. Furthermore, single tweets, even if they have none of these issues, are usually short and often do not contain much context, making them difficult to work with. To overcome some of these problems we propose a new type of corpus and investigate strategies for linking news articles to create news-stories and linking tweets to create tweet-stories and hence linking the news-stories to the tweets-stories to create a corpus of linked tweets that contain emotion-bearing markers. We describe the process of collecting tweets and news articles, the annotation process and the problems therein, and show that a thematically-linked corpus aids the classification process. Preprocessing is an important step in classification. However, there is no standard set of steps. As such, we analyse a number of preprocessing steps, evaluating each to establish its contribution and, thus, form the best combination of steps to carry forward into later experiments. We consider both Arabic and English tweets, and whilst there are well-established Natural Language Processing (NLP) tools for English, the same is not true for Arabic. As part of this work we also evaluate a new Arabic tagger specifically for tweets, and a stemmer, and compare the results to other methods. The major contribution of this thesis is a new type of classifier based on conditional probabilities that are used to build a lexicon of scores that indicate the importance of a word to a specific emotion. We show that incorporating automatic mechanisms for autocorrection, by removing words that are unhelpful in an emotion, and calculating individual thresholds for each emotion, improves classifier performance. To the best of our knowledge, this is the first time these ideas have been explored. The results of this classifier, named CENTEMENT, are compared to other common algorithms such as K-nearest Neighbours (KNN), Support Vector Machine (SVM), and two different configurations of neural networks. We also evaluate a number of other datasets and demonstrate that our algorithm is robust and performs consistently well. The results are encouraging: our approach led to appreciably better performance than currently established classifiers and also many of the latest state-of-the-art classifiers. To further test the robustness of the classifier, it was entered into the worldwide emotion-classification… Advisors/Committee Members: Ramsay, Allan.

Subjects/Keywords: multi-emotion classification; emotion analysis

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APA (6^th Edition):

Ahmad, T. N. (2020). CLASSIFICATION OF TWEETS USING MULTIPLE THRESHOLDS WITH SELF-CORRECTION AND WEIGHTED CONDITIONAL PROBABILITIES. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323659

Chicago Manual of Style (16^th Edition):

Ahmad, Tariq Naseer. “CLASSIFICATION OF TWEETS USING MULTIPLE THRESHOLDS WITH SELF-CORRECTION AND WEIGHTED CONDITIONAL PROBABILITIES.” 2020. Doctoral Dissertation, University of Manchester. Accessed April 23, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323659.

MLA Handbook (7^th Edition):

Ahmad, Tariq Naseer. “CLASSIFICATION OF TWEETS USING MULTIPLE THRESHOLDS WITH SELF-CORRECTION AND WEIGHTED CONDITIONAL PROBABILITIES.” 2020. Web. 23 Apr 2021.

Vancouver:

Ahmad TN. CLASSIFICATION OF TWEETS USING MULTIPLE THRESHOLDS WITH SELF-CORRECTION AND WEIGHTED CONDITIONAL PROBABILITIES. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2021 Apr 23]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323659.

Council of Science Editors:

Ahmad TN. CLASSIFICATION OF TWEETS USING MULTIPLE THRESHOLDS WITH SELF-CORRECTION AND WEIGHTED CONDITIONAL PROBABILITIES. [Doctoral Dissertation]. University of Manchester; 2020. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323659

NSYSU

2. Huang, Bor-Chen. Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications.

Degree: Master, Computer Science and Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224

► As the social network gains more popular, people would like to find the related information from social nets to figure out some signs before or… (more)

Subjects/Keywords: Negative emotion detection; Data mining; Emotion classification

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APA (6^th Edition):

Huang, B. (2015). Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Huang, Bor-Chen. “Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications.” 2015. Thesis, NSYSU. Accessed April 23, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Huang, Bor-Chen. “Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications.” 2015. Web. 23 Apr 2021.

Vancouver:

Huang B. Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications. [Internet] [Thesis]. NSYSU; 2015. [cited 2021 Apr 23]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang B. Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New South Wales

3. Wataraka Gamage, Kalani. Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems.

Degree: Electrical Engineering & Telecommunications, 2018, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true

► Speech-based emotion recognition is a research field of growing interest, which aims to identifyhuman emotions based on speech. The main contributions of this thesis revolve… (more)

▼ Speech-based emotion recognition is a research field of growing interest, which aims to identifyhuman emotions based on speech. The main contributions of this thesis revolve around the use of verbaland non-verbal vocalisation cues for speech-based emotion recognition, which is complementary topopularly used acoustic features for both emotion classification and continuous emotion predictiontasks.This thesis initially explores the supra-segmental feature representations generated by thevectorisation of the Mel-frequency cepstral coefficient frame level feature distribution models foremotion classification, which is an alternative to the default acoustic supra-segmental features. Next,the thesis focuses on the development of approaches for incorporating the emotional saliency andpronunciation of verbal cues (lexical features) for emotion classification.Apart from lexical features, non-verbal vocal events such as laughter, sighs, and expressions suchas “grrr!”, “oh!”, and disfluency patterns including filled pauses such as “hmm” are identified withinthe linguistic feature domain. These elements of speech are instrumental in portraying both voluntaryand involuntary emotions in human communication. Despite this, they have not been used for automaticemotion recognition in a completely automatic manner, and their effect on emotion recognition has notyet been adequately analysed. This thesis proposes and develops several models to utilise emotionallysalient linguistic cues, including non-verbal gestures and disfluencies, implicitly for emotionclassification and continuous emotion prediction tasks. This is achieved without the need for taggedand time aligned non-verbal vocalisation labels. The proposed novel approaches allow emotionrecognition systems to utilise linguistic information independent of manual transcripts or automaticspeech recognition.Inspired by the analysis of the influence of non-verbal vocalisations for continuous emotionprediction, as well as emotion psychology concepts related to the symbolic reference function of suchexpressions, this thesis proposes a novel view of continuous emotion prediction leading to thedevelopment of a transparent framework for continuous emotion prediction. This framework ismodelled as a time-invariant filter array for continuous emotion prediction, and distinct from thepointwise regression mapping taken by traditional approaches.All proposed approaches are extensively evaluated on state-of-the-art emotion databases. Advisors/Committee Members: Ambikairajah, Eliathamby, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW, sethu, vidhyasaharan, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW.

Subjects/Keywords: Emotion classification; Speech based Emotion Recognition; Continuous Emotion Prediction; Vocal Gestures; Non-verbal vocalizations; Lexical

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APA (6^th Edition):

Wataraka Gamage, K. (2018). Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Wataraka Gamage, Kalani. “Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems.” 2018. Doctoral Dissertation, University of New South Wales. Accessed April 23, 2021. http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Wataraka Gamage, Kalani. “Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems.” 2018. Web. 23 Apr 2021.

Vancouver:

Wataraka Gamage K. Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Apr 23]. Available from: http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true.

Council of Science Editors:

Wataraka Gamage K. Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true

4. Abuhammad, H. Emotion classification using combinations of texture descriptors.

Degree: PhD, 2019, University of Exeter

URL: http://hdl.handle.net/10871/39319

► We present an automated new approach for facial expression recognition of seven emotions. The main objective of this thesis is building a model that can… (more)

▼ We present an automated new approach for facial expression recognition of seven emotions. The main objective of this thesis is building a model that can classify the spontaneous facial expressions, rather than the acted ones, and apply this model images and videos. Moreover, we will investigate if a combination of more than one image feature descriptor will improve the classification rate, and the efficacy of the texture descriptors on videos sequences. Three types of texture features from static images were combined: Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and Dense Speeded Up Robust Features (D-SURF). The resulting features are classified using random forests. The use of random forests allows for the identification of the most important feature types and facial locations for emotion classification. Regions around the eyes, forehead, sides of the nose and mouth are found to be most significant. We classified the important features with random forest and Support Vector Machines. We also found that the classification performance became better than using all of the extracted facial features. We achieved better than state-of-the-art accuracies using multiple texture feature descriptors. Current emotion recognition datasets comprise posed portraits of actors displaying emotions. To evaluate the recognition algorithms on spontaneous facial expressions, we introduced an unposed dataset called the ``Emotional Faces in the Wild'' (eLFW), a citizen-labelling of 1310 faces from the Labelled Faces in the Wild data. To collect this data, we built a website and asked citizens to label photos according to the emotion displayed. The citizens were also asked to label a selection of KDEF faces. We evaluated the common misclassification of the faces, similar to what people do; machine algorithms perform worst regarding distinguishing between sad, angry and fearful expressions. We describe a new weighted voting algorithm for multi-calcification, in which the predictions of the classifiers trained on pairs of classes are combined with weights learned using an evolutionary algorithm. This method yields superior results, particularly for the hard-to-distinguish emotions. The method was applied to the DynEmo video database. We investigated some methods to smooth the classifier predictions in order to exploit temporal continuity emotions and therefore classification error. Several smoothing techniques were investigated and optimised, and we found that the simple moving average and linear fit Lowess smoothing performed best.

Subjects/Keywords: facial Emotion; Texture Descriptors; Emotion Classification; SVM; Random Forests

10 more images…

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APA (6^th Edition):

Abuhammad, H. (2019). Emotion classification using combinations of texture descriptors. (Doctoral Dissertation). University of Exeter. Retrieved from http://hdl.handle.net/10871/39319

Chicago Manual of Style (16^th Edition):

Abuhammad, H. “Emotion classification using combinations of texture descriptors.” 2019. Doctoral Dissertation, University of Exeter. Accessed April 23, 2021. http://hdl.handle.net/10871/39319.

MLA Handbook (7^th Edition):

Abuhammad, H. “Emotion classification using combinations of texture descriptors.” 2019. Web. 23 Apr 2021.

Vancouver:

Abuhammad H. Emotion classification using combinations of texture descriptors. [Internet] [Doctoral dissertation]. University of Exeter; 2019. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10871/39319.

Council of Science Editors:

Abuhammad H. Emotion classification using combinations of texture descriptors. [Doctoral Dissertation]. University of Exeter; 2019. Available from: http://hdl.handle.net/10871/39319

5. Conneau, Anne-Claire. Reconnaissance automatique de l'émotion à partir de signaux EEG : Automatic recognition of emotion from EEG signals.

Degree: Docteur es, Signal et images, 2016, Paris, ENST

URL: http://www.theses.fr/2016ENST0033

► Alors que l’utilisation de l’électro-encéphalographie (EEG) a longtemps été confinée au domaine médical, l’intérêt dansles interfaces cerveau-machine basées sur ces signaux s’est développé au cours… (more)

▼ Alors que l’utilisation de l’électro-encéphalographie (EEG) a longtemps été confinée au domaine médical, l’intérêt dansles interfaces cerveau-machine basées sur ces signaux s’est développé au cours de ces dernières années pour les applications grand public. Les enregistrements EEG ont tout particulièrement retenu l’attention des chercheurs dans le domaine de l’informatique affective, affective computing, dans le but de réaliser des travaux sur l’analyse du comportement humain, et ce plus particulièrement sur la reconnaissance automatique des émotions. Comparé à d’autres modalités utilisées dans de précédents travaux sur la reconnaissance des émotions, telles que la parole, les expressions du visage, les mouvements et d’autres signaux physiologiques, l’EEG a l’avantage de pouvoir capturer des informations liées à l’état émotionnel interne qui n’est pas forcément traduit par des manifestations extérieures observables. La reconnaissance des émotions est habituellement envisagée sous l’angle d’un problème de classification où le choix de caractéristiques appropriées est essentiel dans le but de s’assurer une précision de reconnaissance satisfaisante. Une de nos problématiques repose sur le fait que, dans le cadre des caractéristiques EEG, un consensus n’a toujours pas été réalisé sur un ensemble standard de caractéristiques qui permettrait de garantir une distinction performante des émotions d’un sujet humain. Nous explorons une grande variété de caractéristiques temporelles, spectrales et spatiales pouvant être potentiellement utiles dans le cadre de la reconnaissance des émotions et nous les comparons à d’autres, exposées dans de précédents travaux, en utilisant un protocole expérimental rigoureux. Nous évaluons plus particulièrement l’efficacité de plusieurs caractéristiques spectrales, n’ayant pas été précédemment proposées pour le problème de la classification. Nos résultats montrent que les nouvelles caractéristiques spectrales que nous proposons sont compétitives comparées à celles précédemment utilisées. Elles nous amènent de plus vers une configuration mono-canal performante de la reconnaissance des émotions, ce qui implique un fort potentiel pour les applications destinées au grand public. Au sein des corpus existants et accessibles destinés à la reconnaissance des émotions en informatique affective, l’aspect de la dynamique de l’émotion n’est pas pris en considération. Ces corpus présentent également un manque de variabilité dans les données et ne possèdent les enregistrements que d’un nombre limité de participants. Ces raisons nous ont menées à proposer un corpus multi-modal destiné à l’analyse de l’état émotionnel qui s’attache à répondre au mieux à certaines faiblesses des corpus existants. Nous employons différentes stratégies d’élicitation de l’émotion, par le biais de l’utilisation de stimuli visuels et audio-visuels, et nous proposons également une approche novatrice dans la stratégie d’annotation de l’émotion ressentie, en intégrant en plus de la retranscription de l’émotion ressentie de manière… Advisors/Committee Members: Richard, Gaël (thesis director), Essid, Slim (thesis director).

Subjects/Keywords: EEG; Emotion; Apprentissage automatique; Classification; Corpus; EEG; Emotion; Machine learning; Classification; Dataset

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Conneau, A. (2016). Reconnaissance automatique de l'émotion à partir de signaux EEG : Automatic recognition of emotion from EEG signals. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2016ENST0033

Chicago Manual of Style (16^th Edition):

Conneau, Anne-Claire. “Reconnaissance automatique de l'émotion à partir de signaux EEG : Automatic recognition of emotion from EEG signals.” 2016. Doctoral Dissertation, Paris, ENST. Accessed April 23, 2021. http://www.theses.fr/2016ENST0033.

MLA Handbook (7^th Edition):

Conneau, Anne-Claire. “Reconnaissance automatique de l'émotion à partir de signaux EEG : Automatic recognition of emotion from EEG signals.” 2016. Web. 23 Apr 2021.

Vancouver:

Conneau A. Reconnaissance automatique de l'émotion à partir de signaux EEG : Automatic recognition of emotion from EEG signals. [Internet] [Doctoral dissertation]. Paris, ENST; 2016. [cited 2021 Apr 23]. Available from: http://www.theses.fr/2016ENST0033.

Council of Science Editors:

Conneau A. Reconnaissance automatique de l'émotion à partir de signaux EEG : Automatic recognition of emotion from EEG signals. [Doctoral Dissertation]. Paris, ENST; 2016. Available from: http://www.theses.fr/2016ENST0033

6. Al-Mahdawi, Amer. Automatic emotion recognition in English and Arabic text.

Degree: PhD, 2019, Bangor University

URL: https://research.bangor.ac.uk/portal/en/theses/automatic-emotion-recognition-in-english-and-arabic-text(3bd8b060-4c4a-4b5f-8e35-b86811a94228).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782123

► This study investigated the automatic recognition of emotion in English and Arabic text. We perform experiments with a new method of classification for recognising emotions… (more)

▼ This study investigated the automatic recognition of emotion in English and Arabic text. We perform experiments with a new method of classification for recognising emotions using the Prediction by Partial Matching (PPM) character-based text compression scheme. These experiments involve both document level classification (whether a text of document is emotional or not) and also fine-grained classification such as recognising Ekman's six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three English datasets (the LiveJournal's blogs dataset, Aman's blogs dataset, and Alm's fairy tales dataset) show that the new method signicantly outperforms the traditional word-based text classification methods. The results show that the PPM compression-based classification method is able to distinguish between emotional and non-emotional text with high accuracy, between texts involving Happiness and Sadness emotions (with 79.1% accuracy for Aman's dataset and 76.9% for Alm's datasets) and texts involving Ekman's six basic emotions for the LiveJournal dataset (87.4% accuracy). Results also show that the method outperforms traditional feature-based classifiers such as Naive Bayes and SMO in most cases in terms of accuracy, precision, recall and F-measure. In order to see how well the classifier performs on another language not related to English and also in order to create another Arabic benchmark corpus for future emotion classification experiments, we created a new Iraqi Arabic Emotion Corpus (IAEC) dataset annotated according to Ekman's basic emotions. This dataset is composed of Facebook posts written in the Iraqi dialect. We evaluated the quality of this dataset using four external judges which resulted in an average inter-annotation agreement of 0.751. We then explored six different supervised machine learning methods to test the new dataset. We used standard Weka classifiers ZeroR, J48, Naive Bayes, Multinomial Naive Bayes for Text and SMO. We compared these results with our compression-based classifier PPM. Our study reveals that the PPM classifier significantly outperforms the other classifiers for the new dataset achieving the highest results in terms of accuracy, precision, recall, and Fmeasure. We also designed and investigated another new classification technique motivated by information divergence to recognize Ekman's emotions in text. We used the three datasets written in the English Language and the one in the Arabic Language to evaluate the new method. The new method was able to achieve a better result for Alm's dataset in terms of accuracy, precision, recall and F-measure than PPM and standard Weka classifiers. The new method also outperforms all standard Weka classifiers for all four datasets. Finally, these results show that our proposed technique is promising as an alternative technique for English and Arabic text categorization in general.

Subjects/Keywords: PPM; text categorisation; text classification; emotion recognition; classification

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APA (6^th Edition):

Al-Mahdawi, A. (2019). Automatic emotion recognition in English and Arabic text. (Doctoral Dissertation). Bangor University. Retrieved from https://research.bangor.ac.uk/portal/en/theses/automatic-emotion-recognition-in-english-and-arabic-text(3bd8b060-4c4a-4b5f-8e35-b86811a94228).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782123

Chicago Manual of Style (16^th Edition):

Al-Mahdawi, Amer. “Automatic emotion recognition in English and Arabic text.” 2019. Doctoral Dissertation, Bangor University. Accessed April 23, 2021. https://research.bangor.ac.uk/portal/en/theses/automatic-emotion-recognition-in-english-and-arabic-text(3bd8b060-4c4a-4b5f-8e35-b86811a94228).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782123.

MLA Handbook (7^th Edition):

Al-Mahdawi, Amer. “Automatic emotion recognition in English and Arabic text.” 2019. Web. 23 Apr 2021.

Vancouver:

Al-Mahdawi A. Automatic emotion recognition in English and Arabic text. [Internet] [Doctoral dissertation]. Bangor University; 2019. [cited 2021 Apr 23]. Available from: https://research.bangor.ac.uk/portal/en/theses/automatic-emotion-recognition-in-english-and-arabic-text(3bd8b060-4c4a-4b5f-8e35-b86811a94228).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782123.

Council of Science Editors:

Al-Mahdawi A. Automatic emotion recognition in English and Arabic text. [Doctoral Dissertation]. Bangor University; 2019. Available from: https://research.bangor.ac.uk/portal/en/theses/automatic-emotion-recognition-in-english-and-arabic-text(3bd8b060-4c4a-4b5f-8e35-b86811a94228).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782123

University of Houston

7. Neerkaje, Shrivatsa. Estimating the Emotional Content of an Image from the Observer's Eye Scan Patern.

Degree: MSin Electrical Engineering, Electrical Engineering, University of Houston

URL: http://hdl.handle.net/10657/3678

► The aim of the study is to predict the emotional gist of the image, namely the level of arousal (low or high) and kind of… (more)

Subjects/Keywords: Emotion Classification; Predictive analytics

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APA (6^th Edition):

Neerkaje, S. (n.d.). Estimating the Emotional Content of an Image from the Observer's Eye Scan Patern. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3678

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16^th Edition):

Neerkaje, Shrivatsa. “Estimating the Emotional Content of an Image from the Observer's Eye Scan Patern.” Masters Thesis, University of Houston. Accessed April 23, 2021. http://hdl.handle.net/10657/3678.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7^th Edition):

Neerkaje, Shrivatsa. “Estimating the Emotional Content of an Image from the Observer's Eye Scan Patern.” Web. 23 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Neerkaje S. Estimating the Emotional Content of an Image from the Observer's Eye Scan Patern. [Internet] [Masters thesis]. University of Houston; [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10657/3678.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

Neerkaje S. Estimating the Emotional Content of an Image from the Observer's Eye Scan Patern. [Masters Thesis]. University of Houston; Available from: http://hdl.handle.net/10657/3678

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Delft University of Technology

8. Rietveld, T.M. (author). The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features.

Degree: 2020, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d

► Continuous affective self-reports are intrusive and expensive to acquire, forcing researchers to use alternative labels for the construction of their predictive models. The most predominantly… (more)

Subjects/Keywords: Affective Computing; Emotion Classification; Self-reports; Machine Learning; Weakly Supervised Learning

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APA (6^th Edition):

Rietveld, T. M. (. (2020). The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d

Chicago Manual of Style (16^th Edition):

Rietveld, T M (author). “The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features.” 2020. Masters Thesis, Delft University of Technology. Accessed April 23, 2021. http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d.

MLA Handbook (7^th Edition):

Rietveld, T M (author). “The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features.” 2020. Web. 23 Apr 2021.

Vancouver:

Rietveld TM(. The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Apr 23]. Available from: http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d.

Council of Science Editors:

Rietveld TM(. The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d

9. Ferro, Adelino Rafael Mendes. Speech emotion recognition through statistical classification.

Degree: 2017, RCAAP

URL: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ucp.pt:10400.14/22817

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O propósito desta dissertação é a discussão do reconhecimento de emoção na voz. Para este fim, criou-se uma base de dados validada de discurso emocional… (more)

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O propósito desta dissertação é a discussão do reconhecimento de emoção na voz. Para este fim, criou-se uma base de dados validada de discurso emocional simulado Português, intitulada European Portuguese Emotional Discourse Database (EPEDD) e foram operados algoritmos de classificação estatística nessa base de dados. EPEDD é uma base de dados simulada, caracterizada por pequenos discursos (5 frases longas, 5 frases curtas e duas palavras), todos eles pronunciados por 8 atores—ambos os sexos igualmente representados—em 9 diferentes emoções (raiva, alegria, nojo, excitação, apatia, medo, surpresa, tristeza e neutro), baseadas no modelo de emoções de Lövheim. Concretizou-se uma avaliação de 40% da base de dados por avaliadores inexperientes, filtrando 60% dos pequenos discursos, com o intuito de criar uma base de dados validada. A base de dados completa contem 718 instâncias, enquanto que a base de dados validada contém 116 instâncias. A qualidade média de representação teatral, numa escala de a 5 foi avaliada como 2,3. A base de dados validada é composta por discurso emocional cujas emoções são reconhecidas com uma taxa média de 69,6%, por avaliadores inexperientes. A raiva tem a taxa de reconhecimento mais elevada com 79,7%, enquanto que o nojo, a emoção cuja taxa de reconhecimento é a mais baixa, consta com 40,5%. A extração de características e a classificação estatística foi realizada respetivamente através dos softwares Opensmile e Weka. Os algoritmos foram operados na base dados original e na base de dados avaliada, tendo sido obtidos os melhores resultados através de SVMs, respetivamente com 48,7% e 44,0%. A apatia obteve a taxa de reconhecimento mais elevada com 79,0%, enquanto que a excitação obteve a taxa de reconhecimento mais baixa com 32,9%.

The purpose of this dissertation is to discuss speech emotion recognition. It was created a validated acted Portuguese emotional speech database, named European Portuguese Emotional Discourse Database (EPEDD), and statistical classification algorithms have been applied on it. EPEDD is an acted database, featuring 12 utterances (2 single-words, 5 short sentences and 5 long sentences) per actor and per emotion, 8 actors, both genders equally represented, and 9 emotions (anger, joy, disgust, excitement, fear, apathy, surprise, sadness and neutral), based on Lövheim’s emotion model. We had 40% of the database evaluated by unexperienced evaluators, enabling us to produce a validated one, filtering 60% of the evaluated utterances. The full database contains 718 instances, while the validated one contains 116 instances. The average acting quality of the original database was evaluated, in a scale from 1 to 5, as 2,3. The validated database is composed by emotional utterances that have their emotions recognized on average at a 69,6% rate, by unexperienced judges. Anger had the highest recognition rate at 79,7%, while disgust had the lowest recognition rate at 40,5%. Feature extraction and statistical classification algorithms were performed respectively applying Opensmile…

Advisors/Committee Members: Pestana, Pedro.

Subjects/Keywords: Theories of emotion; Speech emotion recognition; Emotional speech; Speech database; Statistical classification; SVM; Random Forests; ANN; Domínio/Área Científica::Humanidades::Artes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ferro, A. R. M. (2017). Speech emotion recognition through statistical classification. (Thesis). RCAAP. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ucp.pt:10400.14/22817

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ferro, Adelino Rafael Mendes. “Speech emotion recognition through statistical classification.” 2017. Thesis, RCAAP. Accessed April 23, 2021. http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ucp.pt:10400.14/22817.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ferro, Adelino Rafael Mendes. “Speech emotion recognition through statistical classification.” 2017. Web. 23 Apr 2021.

Vancouver:

Ferro ARM. Speech emotion recognition through statistical classification. [Internet] [Thesis]. RCAAP; 2017. [cited 2021 Apr 23]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ucp.pt:10400.14/22817.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ferro ARM. Speech emotion recognition through statistical classification. [Thesis]. RCAAP; 2017. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ucp.pt:10400.14/22817

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Université de Grenoble

10. Mathieu, Nicolas. Traitement neurocognitif des émotions au cours du vieillissement : étude de l'"effet de positivité" et ses conséquences : Neurocognitive processing of emotion during aging : study of "positivity effect" and its consequences : Behavioral and electroencephalographic assessments.

Degree: Docteur es, Sciences cognitives, psychologie et neurocognition, 2013, Université de Grenoble

URL: http://www.theses.fr/2013GRENS031

►

Dans le vieillissement « sain », la préférence pour les stimuli positifs augmente par rapport aux stimuli négatifs. Ce phénomène est appelé « effet de… (more)

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Dans le vieillissement « sain », la préférence pour les stimuli positifs augmente par rapport aux stimuli négatifs. Ce phénomène est appelé « effet de positivité » et peut être observé au niveau comportemental et cérébral. L'objectif principal de cette thèse a été de caractériser les effets de l'âge sur les traitements émotionnels afin d'améliorer notre compréhension des effets de positivité. L'objectif sous-jacent a été d'évaluer dans quelles conditions ces effets peuvent conduire à une plus grande vulnérabilité des personnes âgées face à des situations menaçantes. Une première étude en électroencéphalographie a révélé que l'engagement attentionnel pour des scènes naturelles négatives diminue avec l'âge quel que soit leur niveau d'activation dans une tâche de catégorisation affective. A l'inverse, ce dernier reste inchangé pour les situations positives, conduisant à une réduction des biais de négativité. Une deuxième étude en électroencéphalographie, dont le paradigme était similaire à la première étude, a mis en évidence que les biais de négativité restent préservés avec l'âge lorsque l'évaluation des scènes s'effectue sur la dimension de « tendance à l'action ». Une troisième étude révèle que l'attention volontaire sur les situations d'intérêt des personnes âgées (positives) et sur les processus d'évaluation modulés par l'âge est nécessaire à l'émergence des effets de positivité. Parallèlement à ces travaux, une méthodologie innovante est proposée pour la classification d'états émotionnels des personnes jeunes et âgées sur la base de leurs signaux électroencéphalographiques. Nous avons obtenu des résultats encourageants qui suggèrent la possibilité cette méthode pour implémenter des interfaces cerveau-machine pour protéger les personnes âgées d'une éventuelle vulnérabilité en raison des effets de positivité. L'ensemble de ces travaux suggèrent que les effets de positivité sont les conséquences de changements sur le plan motivationnel de l'individu âgé, touchant principalement les processus d'évaluation émotionnel. La personne âgée régulerait ses émotions et diminuerait l'impact des émotions négatives lorsque d'autres motivations plus prioritaires sont absentes.

With aging, the preference for positive stimuli increases compared to negative stimuli. This is called “positivity effect” and it may be observed in both behavior and brain activity. The main goal of this work was to characterized age effects on emotional processing to improve our understood of this positivity effect. The second goal was to evaluate in which conditions these effects could make older people more vulnerable when they are confronted to threatening situations. A first EEG study revealed that the attentional engagement decreased with age for negative stimuli, regardless of their activation level, in an affective categorization task. Conversely, the processing of positive stimuli was preserved with age and, consequently, a reduction of the negativity bias was observed. In a second EEG study, using a similar paradigm to study 1 with the…

Advisors/Committee Members: Campagne, Aurélie (thesis director), Gentaz, Édouard (thesis director).

Subjects/Keywords: Vieillissement; Émotion; Effet de positivité; Attention; EEG; Classification; Aging; Emotion; Positivity effect; Attention; EEG; Emotion recognition; 610

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mathieu, N. (2013). Traitement neurocognitif des émotions au cours du vieillissement : étude de l'"effet de positivité" et ses conséquences : Neurocognitive processing of emotion during aging : study of "positivity effect" and its consequences : Behavioral and electroencephalographic assessments. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2013GRENS031

Chicago Manual of Style (16^th Edition):

Mathieu, Nicolas. “Traitement neurocognitif des émotions au cours du vieillissement : étude de l'"effet de positivité" et ses conséquences : Neurocognitive processing of emotion during aging : study of "positivity effect" and its consequences : Behavioral and electroencephalographic assessments.” 2013. Doctoral Dissertation, Université de Grenoble. Accessed April 23, 2021. http://www.theses.fr/2013GRENS031.

MLA Handbook (7^th Edition):

Mathieu, Nicolas. “Traitement neurocognitif des émotions au cours du vieillissement : étude de l'"effet de positivité" et ses conséquences : Neurocognitive processing of emotion during aging : study of "positivity effect" and its consequences : Behavioral and electroencephalographic assessments.” 2013. Web. 23 Apr 2021.

Vancouver:

Mathieu N. Traitement neurocognitif des émotions au cours du vieillissement : étude de l'"effet de positivité" et ses conséquences : Neurocognitive processing of emotion during aging : study of "positivity effect" and its consequences : Behavioral and electroencephalographic assessments. [Internet] [Doctoral dissertation]. Université de Grenoble; 2013. [cited 2021 Apr 23]. Available from: http://www.theses.fr/2013GRENS031.

Council of Science Editors:

Mathieu N. Traitement neurocognitif des émotions au cours du vieillissement : étude de l'"effet de positivité" et ses conséquences : Neurocognitive processing of emotion during aging : study of "positivity effect" and its consequences : Behavioral and electroencephalographic assessments. [Doctoral Dissertation]. Université de Grenoble; 2013. Available from: http://www.theses.fr/2013GRENS031

Syracuse University

11. Liew, Jasy Suet Yan. FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT.

Degree: PhD, School of Information Studies, 2016, Syracuse University

URL: https://surface.syr.edu/etd/440

► Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the… (more)

▼ Automatic emotion detection in text is concerned with using natural language processing techniques to recognize emotions expressed in written discourse. Endowing computers with the ability to recognize emotions in a particular kind of text, microblogs, has important applications in sentiment analysis and affective computing. In order to build computational models that can recognize the emotions represented in tweets we need to identify a set of suitable emotion categories. Prior work has mainly focused on building computational models for only a small set of six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). This thesis describes a taxonomy of 28 emotion categories, an expansion of these six basic emotions, developed inductively from data. This set of 28 emotion categories represents a set of fine-grained emotion categories that are representative of the range of emotions expressed in tweets, microblog posts on Twitter. The ability of humans to recognize these fine-grained emotion categories is characterized using inter-annotator reliability measures based on annotations provided by expert and novice annotators. A set of 15,553 human-annotated tweets form a gold standard corpus, EmoTweet-28. For each emotion category, we have extracted a set of linguistic cues (i.e., punctuation marks, emoticons, emojis, abbreviated forms, interjections, lemmas, hashtags and collocations) that can serve as salient indicators for that emotion category. We evaluated the performance of automatic classification techniques on the set of 28 emotion categories through a series of experiments using several classifier and feature combinations. Our results shows that it is feasible to extend machine learning classification to fine-grained emotion detection in tweets (i.e., as many as 28 emotion categories) with results that are comparable to state-of-the-art classifiers that detect six to eight basic emotions in text. Classifiers using features extracted from the linguistic cues associated with each category equal or better the performance of conventional corpus-based and lexicon-based features for fine-grained emotion classification. This thesis makes an important theoretical contribution in the development of a taxonomy of emotion in text. In addition, this research also makes several practical contributions, particularly in the creation of language resources (i.e., corpus and lexicon) and machine learning models for fine-grained emotion detection in text. Advisors/Committee Members: Howard R. Turtle, Elizabeth D. Liddy.

Subjects/Keywords: emotion categories; fine-grained emotion classification; machine learning; microblog text; sentiment analysis; Twitter; Social and Behavioral Sciences

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APA (6^th Edition):

Liew, J. S. Y. (2016). FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/440

Chicago Manual of Style (16^th Edition):

Liew, Jasy Suet Yan. “FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT.” 2016. Doctoral Dissertation, Syracuse University. Accessed April 23, 2021. https://surface.syr.edu/etd/440.

MLA Handbook (7^th Edition):

Liew, Jasy Suet Yan. “FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT.” 2016. Web. 23 Apr 2021.

Vancouver:

Liew JSY. FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2021 Apr 23]. Available from: https://surface.syr.edu/etd/440.

Council of Science Editors:

Liew JSY. FINE-GRAINED EMOTION DETECTION IN MICROBLOG TEXT. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/440

University of Rochester

12. Yang, Na. Algorithms for affective and ubiquitous sensing systems and for protein structure prediction.

Degree: PhD, 2015, University of Rochester

URL: http://hdl.handle.net/1802/29666

► Rapid development in sensing technologies has facilitated increased design of more affective and ubiquitous sensing environments for humans. Through affective sensing of human emotions and… (more)

▼ Rapid development in sensing technologies has facilitated increased design of more affective and ubiquitous sensing environments for humans. Through affective sensing of human emotions and behaviors, devices can respond accordingly to provide the users with a better human-computer interaction experience. While affective sensing provides electronic devices with a better understanding of humans, ubiquitous sensing provides humans with a better knowledge of their environments. Wireless sensor networks (WSNs) have been proposed for different ubiquitous sensing scenarios over the decades, and in-home monitoring is one of the successful examples that have been widely deployed. Algorithms designed to optimize such in-home sensor networks can also be mapped to other domains. In particular, the problem of optimizing coverage in directional sensor networks can be mapped to the problem of predicting the structure of proteins, an important challenge for bioinformatics research that is needed for effective drug therapy design. In this dissertation, we contribute algorithms to enable affective and ubiquitous sensing systems as well as algorithms to improve protein structure prediction. Accurate acquisition and interpretation of human physical signals are two essential components for affective sensing. In the first part of this dissertation, we develop a speech-based emotion classification system, which uses several one against- all support vector machines with a threshold-based fusion mechanism to combine the individual outputs. A thorough performance evaluation of this system is provided for different test scenarios, including classification using noisy speech samples and samples from real users. Results show that the system achieves a six-emotion decision-level correct classification rate of 80% for an acted dataset with clean speech. Applications for this proposed emotion sensing system range from behavior studies to context-aware electronics design. Fundamental frequency (F0) is one of the speech features used for emotion classification. However, noise is inevitably included during the speech signal’s acquisition. We present a novel noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. We test the performance of the proposed BaNa algorithm using real human speech samples corrupted by different types of noise. Results show that for almost all types of noise and signal-to-noise ratio values investigated, BaNa achieves the lowest Gross Pitch Error rate among all the classic and state-of-the-art algorithms. In the second part of this dissertation, we study the aforementioned in-home monitoring problem, considering energy efficiency for both the monitoring and transmission processes. In particular, we evaluate the performance of different camera and motion sensor placement strategies, and formulate optimization problems to achieve the minimum energy consumption, longest network lifetime, or the lowest monetary cost. In the image…

Subjects/Keywords: Affective computing; Emotion classification; Mood sensing; Protein structure prediction; Ubiquitous computing; Wireless sensor networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yang, N. (2015). Algorithms for affective and ubiquitous sensing systems and for protein structure prediction. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/29666

Chicago Manual of Style (16^th Edition):

Yang, Na. “Algorithms for affective and ubiquitous sensing systems and for protein structure prediction.” 2015. Doctoral Dissertation, University of Rochester. Accessed April 23, 2021. http://hdl.handle.net/1802/29666.

MLA Handbook (7^th Edition):

Yang, Na. “Algorithms for affective and ubiquitous sensing systems and for protein structure prediction.” 2015. Web. 23 Apr 2021.

Vancouver:

Yang N. Algorithms for affective and ubiquitous sensing systems and for protein structure prediction. [Internet] [Doctoral dissertation]. University of Rochester; 2015. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/1802/29666.

Council of Science Editors:

Yang N. Algorithms for affective and ubiquitous sensing systems and for protein structure prediction. [Doctoral Dissertation]. University of Rochester; 2015. Available from: http://hdl.handle.net/1802/29666

Virginia Tech

13. Christie, Israel C. Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity.

Degree: MS, Psychology, 2002, Virginia Tech

URL: http://hdl.handle.net/10919/32903

► The present study investigated autonomic nervous system (ANS) patterning during experimentally manipulated emotion. Film clips previously shown to induce amusement, anger, contentment, disgust, fear, and… (more)

Subjects/Keywords: Emotion; Autonomic Specificity; Multivariate Pattern Classification

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APA (6^th Edition):

Christie, I. C. (2002). Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32903

Chicago Manual of Style (16^th Edition):

Christie, Israel C. “Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity.” 2002. Masters Thesis, Virginia Tech. Accessed April 23, 2021. http://hdl.handle.net/10919/32903.

MLA Handbook (7^th Edition):

Christie, Israel C. “Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity.” 2002. Web. 23 Apr 2021.

Vancouver:

Christie IC. Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity. [Internet] [Masters thesis]. Virginia Tech; 2002. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10919/32903.

Council of Science Editors:

Christie IC. Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity. [Masters Thesis]. Virginia Tech; 2002. Available from: http://hdl.handle.net/10919/32903

Virginia Tech

14. Stephens, Chad Louis. Autonomic Differentiation of Emotions: A Cluster Analysis Approach.

Degree: MS, Psychology, 2007, Virginia Tech

URL: http://hdl.handle.net/10919/79690

► The autonomic specificity of emotion is intrinsic for many major theories of emotion. One of the goals of this study was to validate a standardized… (more)

Subjects/Keywords: Pattern Classification Analysis; Autonomic Specificity; Emotion

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APA (6^th Edition):

Stephens, C. L. (2007). Autonomic Differentiation of Emotions: A Cluster Analysis Approach. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79690

Chicago Manual of Style (16^th Edition):

Stephens, Chad Louis. “Autonomic Differentiation of Emotions: A Cluster Analysis Approach.” 2007. Masters Thesis, Virginia Tech. Accessed April 23, 2021. http://hdl.handle.net/10919/79690.

MLA Handbook (7^th Edition):

Stephens, Chad Louis. “Autonomic Differentiation of Emotions: A Cluster Analysis Approach.” 2007. Web. 23 Apr 2021.

Vancouver:

Stephens CL. Autonomic Differentiation of Emotions: A Cluster Analysis Approach. [Internet] [Masters thesis]. Virginia Tech; 2007. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10919/79690.

Council of Science Editors:

Stephens CL. Autonomic Differentiation of Emotions: A Cluster Analysis Approach. [Masters Thesis]. Virginia Tech; 2007. Available from: http://hdl.handle.net/10919/79690

Kansas State University

15. Mowla, Md Rakibul. Applications of non-invasive brain-computer interfaces for communication and affect recognition.

Degree: PhD, Department of Electrical and Computer Engineering, 2020, Kansas State University

URL: http://hdl.handle.net/2097/40613

► Various assistive technologies are available for people with communication disorders. While these technologies are quite useful for moderate to severe movement impairments, certain progressive diseases… (more)

▼ Various assistive technologies are available for people with communication disorders. While these technologies are quite useful for moderate to severe movement impairments, certain progressive diseases can cause a total locked-in state (TLIS). These conditions include amyotrophic lateral sclerosis (ALS), neuromuscular disease (NMD), and several other disorders that can cause impairment between the neural pathways and the muscles. For people in a locked-in state (LIS), brain-computer interfaces (BCIs) may be the only possible solution. BCIs could help to restore communication to these people, with the help of external devices and neural recordings. The present dissertation investigates the role of latency jitter on BCIs system performance and, at the same time, the possibility of affect recognition using BCIs. BCIs that can recognize human affect are referred to as affective brain-computer interfaces (aBCIs). These aBCIs are a relatively new area of research in affective computing. Estimation of affective states can improve human-computer interaction as well as improve the care of people with severe disabilities. The present work used a publicly available dataset as well as a dataset collected at the Brain and Body Sensing Lab at K-State to assess the effectiveness of EEG recordings in recognizing affective states. This work proposed an extended classifier-based latency estimation (CBLE) method using sparse autoencoders (SAE) to investigate the role of latency jitter on BCI system performance. The recent emergence of autoencoders motivated the present work to develop an SAE based CBLE method. Here, the newly-developed SAE-based CBLE method is applied to a newly-collected dataset. Results from our data showed a significant (p < 0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, the SAE-based CBLE method is also able to predict BCI accuracy. In the aBCI-related investigation, this work explored the effectiveness of different features extracted from EEG to identify the affect of a user who was experiencing affective stimuli. Furthermore, this dissertation reviewed articles that used the Database for Emotion Analysis Using Physiological Signals (DEAP) (i.e., a publicly available affective database) and found that a significant number of studies did not consider the presence of the class imbalance in the dataset. Failing to consider class imbalance creates misleading results. Furthermore, ignoring class imbalance makes comparing results between studies impossible, since different datasets will have different class imbalances. Class imbalance also shifts the chance level. Hence, it is vital to consider class bias while determining if the results are above chance. This dissertation suggests the use of balanced accuracy as a performance metric and its posterior distribution for computing confidence intervals to account for the effect of class imbalance. Advisors/Committee Members: David E. Thompson.

Subjects/Keywords: Brain-computer interfaces (BCIs); Electroencephalogram (EEG); Classification; P300 speller; Emotion recognition; Machine learning

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APA (6^th Edition):

Mowla, M. R. (2020). Applications of non-invasive brain-computer interfaces for communication and affect recognition. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/40613

Chicago Manual of Style (16^th Edition):

Mowla, Md Rakibul. “Applications of non-invasive brain-computer interfaces for communication and affect recognition.” 2020. Doctoral Dissertation, Kansas State University. Accessed April 23, 2021. http://hdl.handle.net/2097/40613.

MLA Handbook (7^th Edition):

Mowla, Md Rakibul. “Applications of non-invasive brain-computer interfaces for communication and affect recognition.” 2020. Web. 23 Apr 2021.

Vancouver:

Mowla MR. Applications of non-invasive brain-computer interfaces for communication and affect recognition. [Internet] [Doctoral dissertation]. Kansas State University; 2020. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/2097/40613.

Council of Science Editors:

Mowla MR. Applications of non-invasive brain-computer interfaces for communication and affect recognition. [Doctoral Dissertation]. Kansas State University; 2020. Available from: http://hdl.handle.net/2097/40613

University of Illinois – Urbana-Champaign

16. Hu, Xiao. Improving music mood classification using lyrics, audio and social tags.

Degree: PhD, 0370, 2011, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/18435

► The affective aspect of music (popularly known as music mood) is a newly emerging metadata type and access point to music information, but it has… (more)

▼ The affective aspect of music (popularly known as music mood) is a newly emerging metadata type and access point to music information, but it has not been well studied in information science. There has yet to be developed a suitable set of mood categories that can reflect the reality of music listening and can be well adopted in the Music Information Retrieval (MIR) community. As music repositories have grown to an unprecedentedly large scale, people call for automatic tools for music classification and recommendation. However, there have been only a few music mood classification systems with suboptimal performances, and most of them are solely based on the audio content of the music. Lyric text and social tags are resources independent of and complementary to audio content but have yet to be fully exploited. This dissertation research takes up these problems and aims to 1) summarize fundamental insights in music psychology that can help information scientists interpret music mood; 2) identify mood categories that are frequently used by real-world music listeners, through an empirical investigation of real-life social tags applied to music; 3) advance the technology in automatic music mood classification by a thorough investigation on lyric text analysis and the combination of lyrics and audio. Using linguistic resources and human expertise, 36 mood categories were identified from the most popular social tags collected from last.fm, a major Western music tagging site. A ground truth dataset of 5,296 songs in 18 mood categories were built with mood labels given by a number of real-life users. Both commonly used text features and advanced linguistic features were investigated, as well as different feature representation models and feature combinations. The best performing lyric feature sets were then compared to a leading audio-based system. In combining lyric and audio sources, both methods of feature concatenation and late fusion (linear interpolation) of classifiers were examined and compared. Finally, system performances on various numbers of training examples and different audio lengths were compared. The results indicate: 1) social tags can help identify mood categories suitable for a real world music listening environment; 2) the most useful lyric features are linguistic features combined with text stylistic features; 3) lyric features outperform audio features in terms of averaged accuracy across all considered mood categories; 4) systems combining lyrics and audio outperform audio-only and lyric-only systems; 5) combining lyrics and audio can reduce the requirement on training data size, both in number of examples and in audio length. Contributions of this research are threefold. On methodology, it improves the state of the art in music mood classification and text affect analysis in the music domain. The mood categories identified from empirical social tags can complement those in theoretical psychology models. In addition, many of the lyric text features examined in this study have never been formally… Advisors/Committee Members: Downie, J. Stephen (advisor), Smith, Linda C. (Committee Chair), Downie, J. Stephen (committee member), Zhai, ChengXiang (committee member), Heidorn, P. Bryan (committee member).

Subjects/Keywords: Music mood classification; Music; Mood; Metadata; Social tags; Lyrics; Affect analysis; Multimodal classification; Emotion theories; Music mood categories; Music information retrieval

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APA (6^th Edition):

Hu, X. (2011). Improving music mood classification using lyrics, audio and social tags. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18435

Chicago Manual of Style (16^th Edition):

Hu, Xiao. “Improving music mood classification using lyrics, audio and social tags.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 23, 2021. http://hdl.handle.net/2142/18435.

MLA Handbook (7^th Edition):

Hu, Xiao. “Improving music mood classification using lyrics, audio and social tags.” 2011. Web. 23 Apr 2021.

Vancouver:

Hu X. Improving music mood classification using lyrics, audio and social tags. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/2142/18435.

Council of Science Editors:

Hu X. Improving music mood classification using lyrics, audio and social tags. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18435

17. Leandro Yukio Mano Alves. FlexPersuade - Explorando uma abordagem flexível em softwares de persuasão: um estudo de caso com players de música.

Degree: 2016, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112016-112815/

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Estudos atuais na área de Interação Humano-Computador evidenciam a importância de se considerar aspectos emocionais na interação com sistemas computacionais. Acredita-se que ao permitir agentes… (more)

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Estudos atuais na área de Interação Humano-Computador evidenciam a importância de se considerar aspectos emocionais na interação com sistemas computacionais. Acredita-se que ao permitir agentes artificiais identificar emoções de usuários, em uma interação humano-computador, torna-se possível induzir e despertar emoções a fim de estimulá-los em suas atividades. Um dos grandes desafios dos pesquisadores em Interação humano-computador é prover sistemas capazes de reconhecer, interpretar e reagir de modo inteligente e sensível às emoções do usuário, para atender aos requisitos do maior número possível de indivíduos; um dos caminhos que se apresenta é o desenvolvimento de sistemas flexíveis. O principal objetivo de se promover essa interação emotiva é contribuir para o aumento da coerência, consistência e credibilidade das reações e respostas computacionais providas durante a interação humana via interface humano-computador. Nesse contexto, surge a oportunidade de explorar sistemas computacionais capazes de identificar e inferir o estado emocional do usuário em tempo de execução. Este projeto tem como objetivo desenvolver e avaliar um modelo que possa: i.) identificar o estado emocional do usuário; ii.) prover um mecanismo de persuasão com vistas a mudar o estado emocional do usuário (com um estudo de caso em player de música) e; iii.) explorar a abordagem flexível na persuasão (de acordo com o estado emocional particular de cada usuário) através de mecanismos persuasivos que poderão variar entre um player de música, jogos e/ou vídeos. Assim, ao longo do estudo, o modelo baseado em Comitê de Classificação se mostrou eficiente na identificação das emoções básicas (alegria, aversão, medo, neutro, raiva, surpresa e tristeza) com média de acurácia superior a 80% e, ainda, observou-se a satisfação dos usuários mediante a aplicação do modelo com o player de música.

Current studies in the field of Human-Computer Interaction highlight the relevance of emotional aspects while interacting with computers systems. It is believed that allowing intelligent agents to identify users emotions, they can induce and awaken emotions in order to stimulate them while interacting with computers. A major challenge for researchers in human-computer interaction is to provide systems capable of recognizing, interpreting and reacting intelligently and sensitively to the emotions of the user, to meet the requirements of the largest possible number of individuals. One of the ways presented in this project is the development of flexible systems to meet a large number of emotions/behaviors. The main objective of promoting this emotional interaction is to contribute to increasing the coherence, consistency and credibility of reactions and computational responses provided during human interaction via human-computer interface. In this context, the opportunity arises to explore computational systems able to identify and infer the emotional state of the user at runtime. This project aims to develop and evaluate a model that can: i.) identify the…

Advisors/Committee Members: Jo Ueyama, Elisa Yumi Nakagawa, Patrícia Rufino Oliveira, Hermes Senger.

Subjects/Keywords: Classificação da emoção; Comitê de classificação; FaceTracker; Interação humano-computador (IHC); Emotion classification; Ensemble of classification; FaceTracker; Human- computer interaction (HCI)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Alves, L. Y. M. (2016). FlexPersuade - Explorando uma abordagem flexível em softwares de persuasão: um estudo de caso com players de música. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112016-112815/

Chicago Manual of Style (16^th Edition):

Alves, Leandro Yukio Mano. “FlexPersuade - Explorando uma abordagem flexível em softwares de persuasão: um estudo de caso com players de música.” 2016. Masters Thesis, University of São Paulo. Accessed April 23, 2021. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112016-112815/.

MLA Handbook (7^th Edition):

Alves, Leandro Yukio Mano. “FlexPersuade - Explorando uma abordagem flexível em softwares de persuasão: um estudo de caso com players de música.” 2016. Web. 23 Apr 2021.

Vancouver:

Alves LYM. FlexPersuade - Explorando uma abordagem flexível em softwares de persuasão: um estudo de caso com players de música. [Internet] [Masters thesis]. University of São Paulo; 2016. [cited 2021 Apr 23]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112016-112815/.

Council of Science Editors:

Alves LYM. FlexPersuade - Explorando uma abordagem flexível em softwares de persuasão: um estudo de caso com players de música. [Masters Thesis]. University of São Paulo; 2016. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18112016-112815/

University of Southern California

18. Mower, Emily K. Emotions in engineering: methods for the interpretation of ambiguous emotional content.

Degree: PhD, Electrical Engineering, 2010, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2316

► Emotion has intrigued researchers for generations. This fascination has permeated the engineering community, motivating the development of affective computational models for the classification of affective… (more)

Subjects/Keywords: emotion; perception; emotion profiles; emotion classification; emotion representation; audio-visual emotion; audio-visual emotion perception; multimodal emotion expression; facial emotion expression; McGurk effect; hidden Markov model; agglomerative hierarchical clustering; expressive animation; multimodality; affective computing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mower, E. K. (2010). Emotions in engineering: methods for the interpretation of ambiguous emotional content. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2316

Chicago Manual of Style (16^th Edition):

Mower, Emily K. “Emotions in engineering: methods for the interpretation of ambiguous emotional content.” 2010. Doctoral Dissertation, University of Southern California. Accessed April 23, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2316.

MLA Handbook (7^th Edition):

Mower, Emily K. “Emotions in engineering: methods for the interpretation of ambiguous emotional content.” 2010. Web. 23 Apr 2021.

Vancouver:

Mower EK. Emotions in engineering: methods for the interpretation of ambiguous emotional content. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2021 Apr 23]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2316.

Council of Science Editors:

Mower EK. Emotions in engineering: methods for the interpretation of ambiguous emotional content. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2316

19. Wang, Wenbo. Automatic Emotion Identification from Text.

Degree: PhD, Computer Science and Engineering PhD, 2015, Wright State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=wright1440974400

► People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further,… (more)

▼ People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse.Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people's emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that usually involves at least six basic emotions. Text describing an event or situation that causes the emotion can be devoid of explicit emotion-bearing words, thus the distinction between different emotions can be very subtle, which makes it difficult to glean emotions purely by keywords. ii) Manual annotation of emotion data by human experts is very labor-intensive and error-prone. iii) Existing labeled emotion datasets are relatively small, which fails to provide a comprehensive coverage of emotion-triggering events and situations.This dissertation aims at understanding the emotion identification problem and developing general techniques to tackle the above challenges. First, to address the challenge of fine-grained emotion classification, we investigate a variety of lexical, syntactic, knowledge-based, context-based and class-specific features, and show how much these features contribute to the performance of the machine learning classifiers. We also propose a method that automatically extracts syntactic patterns to build a rule-based classifier to improve the accuracy of identifying minority emotions. Second, to deal with the challenge of manual annotation, we leverage emotion hashtags to harvest Twitter "big data" and collect millions of self-labeled emotion tweets, the labeling quality of which is further improved by filtering heuristics. We discover that the size of the training data plays an important role in emotion identification task as it provides a comprehensive coverage of different emotion-triggering events/situations. Further, the unigram and bigram features alone can achieve a performance that is competitive with the best performance of using a combination of ngram, knowledge-based and syntactic features. Third, to handle the paucity of the labeled emotion datasets in many domains, we seek to exploit the abundant self-labeled tweet collection to improve emotion identification in text from other domains, e.g., blog posts, fairy tales. We propose an effective data selection approach to iteratively select source… Advisors/Committee Members: Sheth, Amit (Advisor).

Subjects/Keywords: Computer Science; Emotion Identification; Emotion Classification; Emotion Adaptation; Self-labeled Data Creation; Emotion Analysis

…classification; that is, classifying textual units into different emotion categories such as joy, anger… …hybrid emotion classification approaches in Section 2.5. Finally, we talk about recent studies… …Self-labeled Emotion Data Creation Many emotion classification techniques require annotated… …on large datasets remains a challenge. 2.4 Supervised Emotion Classification Supervised… …labels. Tokuhisa et al. (2008) apply a twostep approach for emotion classification: a…

2 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, W. (2015). Automatic Emotion Identification from Text. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1440974400

Chicago Manual of Style (16^th Edition):

Wang, Wenbo. “Automatic Emotion Identification from Text.” 2015. Doctoral Dissertation, Wright State University. Accessed April 23, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1440974400.

MLA Handbook (7^th Edition):

Wang, Wenbo. “Automatic Emotion Identification from Text.” 2015. Web. 23 Apr 2021.

Vancouver:

Wang W. Automatic Emotion Identification from Text. [Internet] [Doctoral dissertation]. Wright State University; 2015. [cited 2021 Apr 23]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1440974400.

Council of Science Editors:

Wang W. Automatic Emotion Identification from Text. [Doctoral Dissertation]. Wright State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1440974400

20. Banerjee, Debrup. Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection.

Degree: PhD, Electrical/Computer Engineering, 2017, Old Dominion University

URL: 9780438021969 ; https://digitalcommons.odu.edu/ece_etds/31

► Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics… (more)

▼ Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a widely accepted means of diagnosis, but patients are often embarrassed to get diagnosed at clinics. The speech signal based system is a recently developed alternative. Unfortunately,PTSD speech corpora are limited in size which presents difficulties in training complex diagnostic models. This dissertation proposed sparse coding methods and deep belief network models for emotional state identification and PTSD diagnosis. It also includes an additional transfer learning strategy for PTSD diagnosis. Deep belief networks are complex models that cannot work with small data like the PTSD speech database. Thus, a transfer learning strategy was adopted to mitigate the small data problem. Transfer learning aims to extract knowledge from one or more source tasks and apply the knowledge to a target task with the intention of improving the learning. It has proved to be useful when the target task has limited high quality training data. We evaluated the proposed methods on the speech under simulated and actual stress database (SUSAS) for emotional state recognition and on two PTSD speech databases for PTSD diagnosis. Experimental results and statistical tests showed that the proposed models outperformed most state-of-the-art methods in the literature and are potentially efficient models for emotional state recognition and PTSD diagnosis. Advisors/Committee Members: Jiang Li, Frederic McKenzie, Dean Krusienski, Vishnu Lakdawala.

Subjects/Keywords: Deep learning; Emotion classification; Emotion recognition; Machine learning; Sparse code; Speech features; Artificial Intelligence and Robotics; Computer Engineering; Electrical and Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Banerjee, D. (2017). Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection. (Doctoral Dissertation). Old Dominion University. Retrieved from 9780438021969 ; https://digitalcommons.odu.edu/ece_etds/31

Chicago Manual of Style (16^th Edition):

Banerjee, Debrup. “Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection.” 2017. Doctoral Dissertation, Old Dominion University. Accessed April 23, 2021. 9780438021969 ; https://digitalcommons.odu.edu/ece_etds/31.

MLA Handbook (7^th Edition):

Banerjee, Debrup. “Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection.” 2017. Web. 23 Apr 2021.

Vancouver:

Banerjee D. Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection. [Internet] [Doctoral dissertation]. Old Dominion University; 2017. [cited 2021 Apr 23]. Available from: 9780438021969 ; https://digitalcommons.odu.edu/ece_etds/31.

Council of Science Editors:

Banerjee D. Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection. [Doctoral Dissertation]. Old Dominion University; 2017. Available from: 9780438021969 ; https://digitalcommons.odu.edu/ece_etds/31

21. Romih, Anamarija. Učinki treninga regulacije čustev pri učencih osnovne šole.

Degree: 2017, Univerza v Mariboru

URL: https://dk.um.si/IzpisGradiva.php?id=68689 ; https://dk.um.si/Dokument.php?id=119493&dn= ; https://plus.si.cobiss.net/opac7/bib/23478792?lang=sl

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Regulacija čustev je ključna spretnost v življenju. Napoveduje splošno prilagoditev, kakovost socialnih odnosov, uspešnost in psihično ter fizično zdravje. Pri otrocih je spodbujanje spretnosti regulacije… (more)

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Regulacija čustev je ključna spretnost v življenju. Napoveduje splošno prilagoditev, kakovost socialnih odnosov, uspešnost in psihično ter fizično zdravje. Pri otrocih je spodbujanje spretnosti regulacije čustev še posebno pomembno, saj vzorce regulacije prenesejo v nadaljnje soočanje z življenjem in so najdovzetnejši za učenje. V pričujoči raziskavi smo želeli preveriti, ali ima trening regulacije čustev za učence 5. razreda pozitivne učinke na njihovo sposobnost regulacije čustev, socialne spretnosti, vedenja pozunanjanja in ponotranjanja, socialne težave, težave s pozornostjo in šolsko kompetentnost. 15 učencev, starih 10 in 11 let, je bilo udeleženih v 11-tedenskem treningu regulacije čustev (enkrat tedensko po dve šolski uri). 23 učencev je sodelovalo v kontrolni skupini. Pri obeh skupinah smo pred in po treningu ocenili preučevane spremenljivke. Vprašalnike so izpolnili učenci, njihovi starši in razredničarke. Rezultati so pokazali, da trening ni imel pomembnega učinka na regulacijo čustev, sovražno gospodovalnost in sebičnost (socialna nespretnost), vedenja pozunanjanja (agresivnost, delinkventnost), socialne težave, težave s pozornostjo in šolsko kompetentnost (uspeh, količina učenja in trud). Pomemben učinek treninga pa se je pokazal pri treh spremenljivkah: sociabilnosti, socialni občutljivosti in vedenjih ponotranjanja (depresivnost, anksioznost, somatske pritožbe), ocenjenih s strani staršev. Učenci eksperimentalne skupine in njihovi starši so menili, da je bil trening učinkovit, da so se učenci nekaj naučili in da naučeno občasno tudi uporabljajo. Predvsem pa so v treningu uživali in vsako srečanje jim je predstavljalo možnost za pogovor in ventiliranje. Učenci so prejeli material in izročke o ključnih vsebinah s treningov z namenom večje vključitve vsebin v njihov vsakdanjik. Predstavljene so omejitve raziskave, uporabna vrednost in predlogi za nadaljnje raziskovanje.

Emotion regulation is a key skill in life. It predicts a general adjustment, quality of social relations, success and mental as well as physical health. Encouragement of the emotion regulation skill in children is especially important, since they transfer the regulation patterns to further confrontations in life and are the most susceptible to learning. In the following research we wanted to examine if emotion regulation training has positive effects on emotion regulation ability, social skills, extrinsic or intrinsic behaviour, social problems, problems with attention and school competence of 5th grade pupils. 15 pupils (aged 10 and 11 years) participated in emotion regulation training that lasted 11 weeks (once a week for two periods). 23 pupils participated in the control group. We evaluated the studied variables in both groups before and after the training. The questionnaires were filled out by the pupils themselves, their parents and class teachers. The results showed that the training did not have an important effect on emotion regulation, hostile domination and selfishness (social ineptness), extrinsic behaviour (aggressiveness,…

Advisors/Committee Members: Košir, Katja.

Subjects/Keywords: regulacija čustev; trening regulacije čustev; učenci; preventivni program; učinki treninga; emotion regulation; emotion regulation training; pupils; precautionary programme; effects of the training; info:eu-repo/classification/udc/37.015.3:159.942(043.2)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Romih, A. (2017). Učinki treninga regulacije čustev pri učencih osnovne šole. (Masters Thesis). Univerza v Mariboru. Retrieved from https://dk.um.si/IzpisGradiva.php?id=68689 ; https://dk.um.si/Dokument.php?id=119493&dn= ; https://plus.si.cobiss.net/opac7/bib/23478792?lang=sl

Chicago Manual of Style (16^th Edition):

Romih, Anamarija. “Učinki treninga regulacije čustev pri učencih osnovne šole.” 2017. Masters Thesis, Univerza v Mariboru. Accessed April 23, 2021. https://dk.um.si/IzpisGradiva.php?id=68689 ; https://dk.um.si/Dokument.php?id=119493&dn= ; https://plus.si.cobiss.net/opac7/bib/23478792?lang=sl.

MLA Handbook (7^th Edition):

Romih, Anamarija. “Učinki treninga regulacije čustev pri učencih osnovne šole.” 2017. Web. 23 Apr 2021.

Vancouver:

Romih A. Učinki treninga regulacije čustev pri učencih osnovne šole. [Internet] [Masters thesis]. Univerza v Mariboru; 2017. [cited 2021 Apr 23]. Available from: https://dk.um.si/IzpisGradiva.php?id=68689 ; https://dk.um.si/Dokument.php?id=119493&dn= ; https://plus.si.cobiss.net/opac7/bib/23478792?lang=sl.

Council of Science Editors:

Romih A. Učinki treninga regulacije čustev pri učencih osnovne šole. [Masters Thesis]. Univerza v Mariboru; 2017. Available from: https://dk.um.si/IzpisGradiva.php?id=68689 ; https://dk.um.si/Dokument.php?id=119493&dn= ; https://plus.si.cobiss.net/opac7/bib/23478792?lang=sl

Universidade Nova

22. Vale, Pedro Miguel Fernandes. The role of artist and genre on music emotion recognition.

Degree: 2017, Universidade Nova

URL: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/26303

► The goal of this study is to classify a dataset of songs according to their emotion and to understand the impact that the artist and… (more)

Subjects/Keywords: Music Emotion Recognition (MER); Music Information Retrieval (MIR); Songs; Emotions; Data Mining; Classification; Support Vector Machines

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vale, P. M. F. (2017). The role of artist and genre on music emotion recognition. (Thesis). Universidade Nova. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/26303

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vale, Pedro Miguel Fernandes. “The role of artist and genre on music emotion recognition.” 2017. Thesis, Universidade Nova. Accessed April 23, 2021. https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/26303.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vale, Pedro Miguel Fernandes. “The role of artist and genre on music emotion recognition.” 2017. Web. 23 Apr 2021.

Vancouver:

Vale PMF. The role of artist and genre on music emotion recognition. [Internet] [Thesis]. Universidade Nova; 2017. [cited 2021 Apr 23]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/26303.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vale PMF. The role of artist and genre on music emotion recognition. [Thesis]. Universidade Nova; 2017. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/26303

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Northeastern University

23. Siegel, Erika Hansen. Fingerprints or populations?: bodily responses during affect and emotion.

Degree: PhD, Department of Psychology, 2016, Northeastern University

URL: http://hdl.handle.net/2047/D20195405

► For as long as scholars have been writing about the human mind they have been speculating on the nature of affect and emotion. Currently, scientists… (more)

▼ For as long as scholars have been writing about the human mind they have been speculating on the nature of affect and emotion. Currently, scientists agree that affect and emotions are tools that evolved for dealing with the challenges of life. Scientists also agree that both affect and emotions involve physiologically-mediated changes in heart rate, respiration, activity of the sweat glands, etc., although the nature of these changes have been debated for over a century. Classical theories hypothesize that certain emotion categories have biological essences resulting in physiological "fingerprints" that are specific to one category and distinct from other categories. An alternative hypothesis, what we call the "populations" hypothesis, suggests that there are no fingerprints for emotion categories. Rather, categories name a population of variable instances that are context sensitive. We present data from two meta-analyses of over 350 published studies of physiological reactivity during instances of emotion and affect in which we directly test whether there are specific biobehavioral patterns that form a distinct "fingerprint" for affect and emotion categories. We utilized traditional univariate meta-analysis alongside multivariate pattern classification to search for fingerprints. In our emotion meta-analysis, both traditional univariate meta-analytic techniques and multivariate pattern classification failed to reveal evidence of distinct autonomic fingerprints for emotion categories. Instead, we found tremendous variation within and across emotion categories. We also found that experimental context explained a significant portion of the variability in many comparisons. We found significant within category variability in our affect meta-analysis as well. However, in our multivariate pattern classification analysis, we were able to classify affect categories slightly above chance, but found no evidence of specific patterns in our univariate results. Overall, our findings did not suggest robust fingerprints for either affect or emotion. Rather, pervasive variation across and within categories suggested that both affect and emotion may be more accurately described as abstract categories that describes a population of variable instances.

Subjects/Keywords: affect; emotion; meta analysis; pattern classification; psychophysiology; Emotions; Physiological aspects; Autonomic nervous system; Physiological aspects; Psychophysiology; Affect (Psychology); Multivariate analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Siegel, E. H. (2016). Fingerprints or populations?: bodily responses during affect and emotion. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20195405

Chicago Manual of Style (16^th Edition):

Siegel, Erika Hansen. “Fingerprints or populations?: bodily responses during affect and emotion.” 2016. Doctoral Dissertation, Northeastern University. Accessed April 23, 2021. http://hdl.handle.net/2047/D20195405.

MLA Handbook (7^th Edition):

Siegel, Erika Hansen. “Fingerprints or populations?: bodily responses during affect and emotion.” 2016. Web. 23 Apr 2021.

Vancouver:

Siegel EH. Fingerprints or populations?: bodily responses during affect and emotion. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/2047/D20195405.

Council of Science Editors:

Siegel EH. Fingerprints or populations?: bodily responses during affect and emotion. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20195405

24. Kamenik, Anja. Stres in uravnavanje čustev pri nadarjenih učencih.

Degree: 2017, Univerza v Mariboru

URL: https://dk.um.si/IzpisGradiva.php?id=66632 ; https://dk.um.si/Dokument.php?id=113620&dn= ; https://plus.si.cobiss.net/opac7/bib/23331592?lang=sl

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Teoretično ozadje: Stres predstavlja prilagodljiv odziv na neke zunanje situacije, ki lahko vodijo k spremembam. Slednje se lahko kažejo na vedenjskem, fizičnem ali mentalnem področju.… (more)

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Teoretično ozadje: Stres predstavlja prilagodljiv odziv na neke zunanje situacije, ki lahko vodijo k spremembam. Slednje se lahko kažejo na vedenjskem, fizičnem ali mentalnem področju. Stres se pojavlja tudi pri nadarjenih učencih, ki se verjetneje s stresom spoprijemajo tako, da uporabljajo strategije spoprijemanja, ki so osredotočene na problem, za njih pa sta značilni tudi psihološka in socialna regulacija. Obstaja premalo raziskav, ki se osredotočajo na preučevanje različnih vidikov stresa in uravnavanja čustev pri nadarjenih učencih. Metoda: V raziskavo smo vključili mladostnike (n = 98), stare od 13 do 15 let. Od tega je bilo 52 nadarjenih in 46 ostalih učencev. Uporabili smo štiri vprašalnike: Ček lista stresorjev, Vprašalnik Načini spoprijemanja s stresom (WCQ I), Vprašalnik rezilientnosti in Vprašalnik emocionalne kompetentnosti (ESCQ). Rezultati: Nadarjeni učenci se soočajo z enakimi vrstami stresorjev kot učenci, ki niso identificirani kot nadarjeni. Učenke ne doživljajo več stresa kot učenci. Med nadarjenimi učenci in učenci, ki niso identificirani kot nadarjeni, pri spoprijemanju s stresom, ki je osredotočeno na problem, ni razlik. Nadarjeni učenci so bolj odporni na stres kot pa učenci, ki niso identificirani kot nadarjeni. Med nadarjenimi in ostalimi učenci v uravnavanju čustev ne prihaja do razlik. Zaključki: Raziskovanje stresa in emocionalne inteligentnosti pri nadarjenih učencih je ključno za pridobitev novih spoznanj na tem področju. S pridobljenimi spoznanji bomo lažje razumeli omenjeni vzorec mladostnikov in na takšen način prispevali k izboljšanju njihovega duševnega zdravja. Pomembno je, da o teh aktualnih izsledkih obveščamo starše, vzgojitelje, učitelje in druge pomembne ljudi v življenju nadarjenih mladostnikov. Ti posamezniki lahko namreč vplivajo na doživljanje stresa, soočanje s stresom, odpornost na stres ter na uravnavanje čustev pri mladostnikih.

Theory: Stress is defined as an adaptable reaction to any outer situations leading to changes which can be indicated in the behavioral, physical or mental fields. Moreover, gifted pupils are faced with stress and they presumably cope with stress by applying strategies of confrontation with the focus on a problem characterized by a typical psychological and social regulation. However, there are not enough researches aimed at researching different viewpoints of stress and emotion regulations in connection with gifted pupils. Method: Our research comprised adolescents (n=98) aged between 13 and 15, however, 52 of them were gifted pupils and 46 other pupils. Four different questionnaires were used: stressor checklist, Ways of coping Questionnaire (WCQ I), Resilience Questionnaire and Emotional Skills and Competence Questionnaire (ESCQ). Results: Gifted pupils face with the same stressors as other pupils who are not identified as gifted. Female pupils do not experience more stress than male pupils. However, there are no differences between gifted pupils and pupils not identified as gifted when coping with stress focused on a problem. Gifted…

Advisors/Committee Members: Bakračevič, Karin.

Subjects/Keywords: nadarjenost; mladostništvo; stres; uravnavanje čustev.; Gift; adolescence; stress; emotion regulations.; info:eu-repo/classification/udc/159.944.4:37.091.212.3(043.2)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Kamenik, A. (2017). Stres in uravnavanje čustev pri nadarjenih učencih. (Masters Thesis). Univerza v Mariboru. Retrieved from https://dk.um.si/IzpisGradiva.php?id=66632 ; https://dk.um.si/Dokument.php?id=113620&dn= ; https://plus.si.cobiss.net/opac7/bib/23331592?lang=sl

Chicago Manual of Style (16^th Edition):

Kamenik, Anja. “Stres in uravnavanje čustev pri nadarjenih učencih.” 2017. Masters Thesis, Univerza v Mariboru. Accessed April 23, 2021. https://dk.um.si/IzpisGradiva.php?id=66632 ; https://dk.um.si/Dokument.php?id=113620&dn= ; https://plus.si.cobiss.net/opac7/bib/23331592?lang=sl.

MLA Handbook (7^th Edition):

Kamenik, Anja. “Stres in uravnavanje čustev pri nadarjenih učencih.” 2017. Web. 23 Apr 2021.

Vancouver:

Kamenik A. Stres in uravnavanje čustev pri nadarjenih učencih. [Internet] [Masters thesis]. Univerza v Mariboru; 2017. [cited 2021 Apr 23]. Available from: https://dk.um.si/IzpisGradiva.php?id=66632 ; https://dk.um.si/Dokument.php?id=113620&dn= ; https://plus.si.cobiss.net/opac7/bib/23331592?lang=sl.

Council of Science Editors:

Kamenik A. Stres in uravnavanje čustev pri nadarjenih učencih. [Masters Thesis]. Univerza v Mariboru; 2017. Available from: https://dk.um.si/IzpisGradiva.php?id=66632 ; https://dk.um.si/Dokument.php?id=113620&dn= ; https://plus.si.cobiss.net/opac7/bib/23331592?lang=sl

25. Zelenik, Aleš. VEČLOČLJIVOSTNO IZLOČANJE ZNAČILK PRI RAZPOZNAVANJU EMOCIJ V GOVORU.

Degree: 2013, Univerza v Mariboru

URL: https://dk.um.si/IzpisGradiva.php?id=39949 ; https://dk.um.si/Dokument.php?id=54369&dn= ; https://plus.si.cobiss.net/opac7/bib/266300928?lang=sl

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V doktorski nalogi obravnavamo problematiko prepoznave emocionalnega govora iz avdio posnetkov. V okviru naloge je za izločanje značilk uporabljenih več različnih širin kratkočasovnih procesnih oken,… (more)

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V doktorski nalogi obravnavamo problematiko prepoznave emocionalnega govora iz avdio posnetkov. V okviru naloge je za izločanje značilk uporabljenih več različnih širin kratkočasovnih procesnih oken, z namenom pridobitve optimalne širine in doseganje najvišje stopnje prepoznave. V dosedanjih raziskavah se največkrat pojavljajo procesna okna širine 20 in 100ms [6], kjer uporaba krajšega okna omogoča boljšo časovno ločljivost, a slabšo frekvenčno ločljivost, medtem ko daljša okna dvignejo frekvenčno ločljivost in poslabšajo časovno ločljivost. V tej točki je definiran nov postopek, ki združi prednosti uporabe ožjih in širših oken in izkorišča prednosti dinamičnega prilagajanja časovne in frekvenčne ločljivosti pri posameznih značilkah. Postopek, poimenovan ESRA, definira koncept večločljivostnega izločanja, izbire in uporabe značilk in pri tem poskrbi za uporabo večločljivostnega koncepta pri razpoznavanju končnih razredov, kjer se za procesiranje uporabi del akustičnega signala, ki vsebuje zvočni govor. Dodatno višanje nivoja uspešnosti prepoznave je doseženo z uporabo normalizacije uporabljenih značilk ter glajenja vrednosti značilk v postprocesiranju. Dodana vrednost pri postopku optimizacije uspešnosti razpoznave je v definiranju algoritma zamenjave končnih razredov, s katerim je bilo doseženo zvišanje uspešnosti najoptimalnejših rezultatov prepoznavanja emocionalnih posnetkov. Za vrednotenje vpliva algoritma na optimizacijo nivoja razpoznave emocionalnega govora sta uporabljeni dve različni območji poimenovani kratko- in dolgočasovno območje, na podlagi katerih poteka izločanje in ocenjevanje od emocij odvisnih značilk govora, z namenom njihove uporabe pri razpoznavanju emocij v govoru. Pri tem sta za potrditev delovanja algoritma uporabljena dva načina generiranja podsetov značilk ter za klasifikacijo štirje različni klasifikatorji (MLP, RF, KNN, GMM). Uporabljeni emocionalni posnetki so del emocionalne govorne baze Interface [18], ki vsebuje igrane posnetke osnovnih šestih emocionalnih razredov (Ekman-ovih velikih šest) in nevtralni govor. Najvišja dosežena uspešnost prepoznave večločljivostnega pristopa je znašala 88,6%, kar je za 3,8% presegalo najboljšo uspešnost enonivojskega pristopa oziroma je bila uspešnost prepoznave za 24,9% višja v relativnem smislu. Podane so primerjave z rezultati uspešnosti dosedanjih raziskav na uporabljeni bazi.

The presented thesis treats the problem of recognizing emotional speech from audio recordings. In order to obtain the optimum processing window width for feature extraction and to achieve the highest level of recognition rates, several different short term processing window widths have been evaluated. In previous studies, the most common process window widths used, were between 20 and 100ms [6], where a shorter window provides better time resolution but poor frequency resolution, while the longer window lifts frequency resolution and worsens time resolution. At this point, we define a new procedure that combines the advantages of narrower and wider windows and takes…

Advisors/Committee Members: Kačič, Zdravko.

Subjects/Keywords: govor; razpoznavanje emocij; segmentacija; večločljivost; speech; emotion recognition; segmentation; multi-resolution; info:eu-repo/classification/udc/004.934:004.383.3(043.3)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zelenik, A. (2013). VEČLOČLJIVOSTNO IZLOČANJE ZNAČILK PRI RAZPOZNAVANJU EMOCIJ V GOVORU. (Doctoral Dissertation). Univerza v Mariboru. Retrieved from https://dk.um.si/IzpisGradiva.php?id=39949 ; https://dk.um.si/Dokument.php?id=54369&dn= ; https://plus.si.cobiss.net/opac7/bib/266300928?lang=sl

Chicago Manual of Style (16^th Edition):

Zelenik, Aleš. “VEČLOČLJIVOSTNO IZLOČANJE ZNAČILK PRI RAZPOZNAVANJU EMOCIJ V GOVORU.” 2013. Doctoral Dissertation, Univerza v Mariboru. Accessed April 23, 2021. https://dk.um.si/IzpisGradiva.php?id=39949 ; https://dk.um.si/Dokument.php?id=54369&dn= ; https://plus.si.cobiss.net/opac7/bib/266300928?lang=sl.

MLA Handbook (7^th Edition):

Zelenik, Aleš. “VEČLOČLJIVOSTNO IZLOČANJE ZNAČILK PRI RAZPOZNAVANJU EMOCIJ V GOVORU.” 2013. Web. 23 Apr 2021.

Vancouver:

Zelenik A. VEČLOČLJIVOSTNO IZLOČANJE ZNAČILK PRI RAZPOZNAVANJU EMOCIJ V GOVORU. [Internet] [Doctoral dissertation]. Univerza v Mariboru; 2013. [cited 2021 Apr 23]. Available from: https://dk.um.si/IzpisGradiva.php?id=39949 ; https://dk.um.si/Dokument.php?id=54369&dn= ; https://plus.si.cobiss.net/opac7/bib/266300928?lang=sl.

Council of Science Editors:

Zelenik A. VEČLOČLJIVOSTNO IZLOČANJE ZNAČILK PRI RAZPOZNAVANJU EMOCIJ V GOVORU. [Doctoral Dissertation]. Univerza v Mariboru; 2013. Available from: https://dk.um.si/IzpisGradiva.php?id=39949 ; https://dk.um.si/Dokument.php?id=54369&dn= ; https://plus.si.cobiss.net/opac7/bib/266300928?lang=sl

26. Radinović, Ksenija. Slogi navezanosti in dimenzije uravnavanja čustev.

Degree: 2020, Univerza v Mariboru

URL: https://dk.um.si/IzpisGradiva.php?id=75697 ; https://dk.um.si/Dokument.php?id=140351&dn= ; https://plus.si.cobiss.net/opac7/bib/25138440?lang=sl

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Uravnavanje čustev ima ključno vlogo v zdravem, prilagojenem delovanju človeka in je predmet raziskovanja mnogih avtorjev, ki povezujejo spoznanja psiholoških in bioloških disciplin. V sodobni… (more)

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Uravnavanje čustev ima ključno vlogo v zdravem, prilagojenem delovanju človeka in je predmet raziskovanja mnogih avtorjev, ki povezujejo spoznanja psiholoških in bioloških disciplin. V sodobni literaturi je na omenjenem področju raziskovanja prevladalo stališče, da se čustvenega razvoja ne da razumeti izven okvirja intimnih odnosov. V tem kontekstu predstavlja klasična teorija navezanosti temelj za razumevanje razvoja sposobnosti za uravnavanje čustev. Namen magistrskega dela je preučiti uravnavanje čustev in navezanost pri odraslih. Zanimalo nas je, ali se različni slogi navezanosti povezujejo s težavami v uravnavanju čustev. Raziskali smo tudi, ali se kaže spol kot moderator povezanosti slogov ne-varne navezanosti s čustvenim zavedanjem. Vzorec je zajemal 229 udeležencev. Za pridobivanje podatkov smo uporabili Lestvico ugotavljanja težav z uravnavanjem čustev (DERS), vprašalnik Doživljanje odnosov z bližnjimi (ECR-R) in Vprašalnik medosebnih odnosov (RQ). V raziskavi smo ugotovili, da se varna navezanost negativno povezuje s težavami v uravnavanju čustev, medtem ko se sloga ne-varne navezanosti (anksiozne, izogibajoče) in težave v uravnavanju čustev povezujejo pozitivno. Glede vloge spola kot moderatorja učinka povezanosti ne-varnih slogov navezanosti s čustvenim zavedanjem smo na podlagi rezultatov ugotovili, da imajo izogibajoče navezani moški več težav s čustvenim zavedanjem kot izogibajoče navezane ženske. Hipoteze o učinku spola pri moderiranju povezanosti anksiozne navezanosti s čustvenim zavedanjem na našem vzorcu nismo podprli.

Emotion regulation plays a key role in the healthy, adjusted functioning of a human and is the subject of research of many authors who find a correlation between the knowledge of psychological and biological disciplines. In the contemporary literature, the aforementioned field of research has been dominated by the view that emotional development cannot be understood beyond the scope of intimate relationships. In this context, classical attachment theory is the foundation for understanding the development of emotion regulation abilities. The purpose of the Master's thesis is to study emotion regulation and attachment in adults. We were interested if different attachment styles are associated with difficulty in regulating emotions. We also examined whether gender is being seen as a moderator of the connection between styles of unsafe attachment with emotional awareness. The sample included 229 participants. To obtain data we used The Difficulties in Emotion Regulation Scale (DERS), The Experiences in Close Relationships-Revised (ECR-R) questionnaire and The Relationship Questionnaire (RQ). The study found that secure attachment correlates negatively with emotion regulation problems, whereas non-secure attachment styles (anxious and avoidant attachment styles) and difficulties in emotion regulation correlate positively. With regard to the role of gender as a moderator of the connection between the association of non-secure attachment styles with emotional awareness, we found that…

Advisors/Committee Members: Cugmas, Zlatka.

Subjects/Keywords: uravnavanje čustev; slogi navezanosti; razlike med spoloma; emotion regulation; attachment styles; gender differences; info:eu-repo/classification/udc/159.942-055.1/.2(043.2)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Radinović, K. (2020). Slogi navezanosti in dimenzije uravnavanja čustev. (Masters Thesis). Univerza v Mariboru. Retrieved from https://dk.um.si/IzpisGradiva.php?id=75697 ; https://dk.um.si/Dokument.php?id=140351&dn= ; https://plus.si.cobiss.net/opac7/bib/25138440?lang=sl

Chicago Manual of Style (16^th Edition):

Radinović, Ksenija. “Slogi navezanosti in dimenzije uravnavanja čustev.” 2020. Masters Thesis, Univerza v Mariboru. Accessed April 23, 2021. https://dk.um.si/IzpisGradiva.php?id=75697 ; https://dk.um.si/Dokument.php?id=140351&dn= ; https://plus.si.cobiss.net/opac7/bib/25138440?lang=sl.

MLA Handbook (7^th Edition):

Radinović, Ksenija. “Slogi navezanosti in dimenzije uravnavanja čustev.” 2020. Web. 23 Apr 2021.

Vancouver:

Radinović K. Slogi navezanosti in dimenzije uravnavanja čustev. [Internet] [Masters thesis]. Univerza v Mariboru; 2020. [cited 2021 Apr 23]. Available from: https://dk.um.si/IzpisGradiva.php?id=75697 ; https://dk.um.si/Dokument.php?id=140351&dn= ; https://plus.si.cobiss.net/opac7/bib/25138440?lang=sl.

Council of Science Editors:

Radinović K. Slogi navezanosti in dimenzije uravnavanja čustev. [Masters Thesis]. Univerza v Mariboru; 2020. Available from: https://dk.um.si/IzpisGradiva.php?id=75697 ; https://dk.um.si/Dokument.php?id=140351&dn= ; https://plus.si.cobiss.net/opac7/bib/25138440?lang=sl

RMIT University

27. He, L. Stress and emotion recognition in natural speech in the work and family environments.

Degree: 2010, RMIT University

URL: http://researchbank.rmit.edu.au/view/rmit:10578

► The speech stress and emotion recognition and classification technology has a potential to provide significant benefits to the national and international industry and society in… (more)

▼ The speech stress and emotion recognition and classification technology has a potential to provide significant benefits to the national and international industry and society in general. The accuracy of an automatic emotion speech and emotion recognition relays heavily on the discrimination power of the characteristic features. This work introduced and examined a number of new linear and nonlinear feature extraction methods for an automatic detection of stress and emotion in speech. The proposed linear feature extraction methods included features derived from the speech spectrograms (SS-CB/BARK/ERB-AE, SS-AF-CB/BARK/ERB-AE, SS-LGF-OFS, SS-ALGF-OFS, SS-SP-ALGF-OFS and SS-sigma-pi), wavelet packets (WP-ALGF-OFS) and the empirical mode decomposition (EMD-AER). The proposed nonlinear feature extraction methods were based on the results of recent laryngological studies and nonlinear modelling of the phonation process. The proposed nonlinear features included the area under the TEO autocorrelation envelope based on different spectral decompositions (TEO-DWT, TEO-WP, TEO-PWP-S and TEO-PWP-G), as well as features representing spectral energy distribution of speech (AUSEES) and glottal waveform (AUSEEG). The proposed features were compared with features based on the classical linear model of speech production including F0, formants, MFCC and glottal time/frequency parameters. Two classifiers GMM and KNN were tested for consistency. The experiments used speech under actual stress from the SUSAS database (7 speakers; 3 female and 4 male) and speech with five naturally expressed emotions (neutral, anger, anxious, dysphoric and happy) from the ORI corpora (71 speakers; 27 female and 44 male). The nonlinear features clearly outperformed all the linear features. The classification results demonstrated consistency with the nonlinear model of the phonation process indicating that the harmonic structure and the spectral distribution of the glottal energy provide the most important cues for stress and emotion recognition in speech. The study also investigated if the automatic emotion recognition can determine differences in emotion expression between parents of depressed adolescents and parents of non-depressed adolescents. It was also investigated if there are differences in emotion expression between mothers and fathers in general. The experiment results indicated that parents of depressed adolescent produce stronger more exaggerated expressions of affect than parents of non-depressed children. And females in general provide easier to discriminate (more exaggerated) expressions of affect than males.

Subjects/Keywords: Fields of Research; stress classification; emotion classification; speech analysis; feature extraction; nonlinear modelling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

He, L. (2010). Stress and emotion recognition in natural speech in the work and family environments. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:10578

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

He, L. “Stress and emotion recognition in natural speech in the work and family environments.” 2010. Thesis, RMIT University. Accessed April 23, 2021. http://researchbank.rmit.edu.au/view/rmit:10578.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

He, L. “Stress and emotion recognition in natural speech in the work and family environments.” 2010. Web. 23 Apr 2021.

Vancouver:

He L. Stress and emotion recognition in natural speech in the work and family environments. [Internet] [Thesis]. RMIT University; 2010. [cited 2021 Apr 23]. Available from: http://researchbank.rmit.edu.au/view/rmit:10578.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

He L. Stress and emotion recognition in natural speech in the work and family environments. [Thesis]. RMIT University; 2010. Available from: http://researchbank.rmit.edu.au/view/rmit:10578

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Victoria

28. Dufour, Isabelle. Improving Music Mood Annotation Using Polygonal Circular Regression.

Degree: Department of Computer Science, 2015, University of Victoria

URL: http://hdl.handle.net/1828/6613

► Music mood recognition by machine continues to attract attention from both academia and industry. This thesis explores the hypothesis that the music emotion problem is… (more)

Subjects/Keywords: Polygonal Circular Regression; Automatic Mood Classification; Audio Features; Music Information Retrieval (MIR); Music Emotion Recognition (MER); Machine Learning; Mood annotation; Content-based audio; valence-arousal; Affective computing; Circular regression; Emotion recognition; Circular model

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Dufour, I. (2015). Improving Music Mood Annotation Using Polygonal Circular Regression. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/6613

Chicago Manual of Style (16^th Edition):

Dufour, Isabelle. “Improving Music Mood Annotation Using Polygonal Circular Regression.” 2015. Masters Thesis, University of Victoria. Accessed April 23, 2021. http://hdl.handle.net/1828/6613.

MLA Handbook (7^th Edition):

Dufour, Isabelle. “Improving Music Mood Annotation Using Polygonal Circular Regression.” 2015. Web. 23 Apr 2021.

Vancouver:

Dufour I. Improving Music Mood Annotation Using Polygonal Circular Regression. [Internet] [Masters thesis]. University of Victoria; 2015. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/1828/6613.

Council of Science Editors:

Dufour I. Improving Music Mood Annotation Using Polygonal Circular Regression. [Masters Thesis]. University of Victoria; 2015. Available from: http://hdl.handle.net/1828/6613

Brno University of Technology

29. Červenec, Radek. Rozpoznávání emocí v česky psaných textech: Recognition of emotions in Czech texts.

Degree: 2014, Brno University of Technology

URL: http://hdl.handle.net/11012/3781

► With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has… (more)

Subjects/Keywords: dolování znalostí; genetický algoritmus; kategorizace; klasifikace; lemmatizace; lemmatizátor; lexikální databáze; rozpoznávání emocí; strojové učení; SVM; trénovací množina; WordNet; annotated corpus; categorization; classification; emotion detection; emotion recognition; feature selection; genetic algorithm; lemmatization; lemmatizer; lexical database; machine learning; SVM; text mining; WordNet

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Červenec, R. (2014). Rozpoznávání emocí v česky psaných textech: Recognition of emotions in Czech texts. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/3781

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Červenec, Radek. “Rozpoznávání emocí v česky psaných textech: Recognition of emotions in Czech texts.” 2014. Thesis, Brno University of Technology. Accessed April 23, 2021. http://hdl.handle.net/11012/3781.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Červenec, Radek. “Rozpoznávání emocí v česky psaných textech: Recognition of emotions in Czech texts.” 2014. Web. 23 Apr 2021.

Vancouver:

Červenec R. Rozpoznávání emocí v česky psaných textech: Recognition of emotions in Czech texts. [Internet] [Thesis]. Brno University of Technology; 2014. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/11012/3781.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Červenec R. Rozpoznávání emocí v česky psaných textech: Recognition of emotions in Czech texts. [Thesis]. Brno University of Technology; 2014. Available from: http://hdl.handle.net/11012/3781

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Harvard University

30. Reyneke, Rupert. Improving Interactive User Experience With Microinteractions: an Application of Biometric and Affect Detection Systems on Landing Pages.

Degree: ALM, 2019, Harvard University

URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42006719

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The essence of User Experience (UX) is to improve the quality of the user’s interaction with and perception of the product or service offered. Measuring… (more)

Subjects/Keywords: AFFDEX; Affectiva; emotions; emotion classification; facial micro-expressions; microinteractions; FACS; facial expression; feedback loop; user experience; iMotions; Tobi; user experience; UX/UI.

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APA (6^th Edition):

Reyneke, R. (2019). Improving Interactive User Experience With Microinteractions: an Application of Biometric and Affect Detection Systems on Landing Pages. (Masters Thesis). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:42006719

Chicago Manual of Style (16^th Edition):

Reyneke, Rupert. “Improving Interactive User Experience With Microinteractions: an Application of Biometric and Affect Detection Systems on Landing Pages.” 2019. Masters Thesis, Harvard University. Accessed April 23, 2021. http://nrs.harvard.edu/urn-3:HUL.InstRepos:42006719.

MLA Handbook (7^th Edition):

Reyneke, Rupert. “Improving Interactive User Experience With Microinteractions: an Application of Biometric and Affect Detection Systems on Landing Pages.” 2019. Web. 23 Apr 2021.

Vancouver:

Reyneke R. Improving Interactive User Experience With Microinteractions: an Application of Biometric and Affect Detection Systems on Landing Pages. [Internet] [Masters thesis]. Harvard University; 2019. [cited 2021 Apr 23]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42006719.

Council of Science Editors:

Reyneke R. Improving Interactive User Experience With Microinteractions: an Application of Biometric and Affect Detection Systems on Landing Pages. [Masters Thesis]. Harvard University; 2019. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42006719

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