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You searched for subject:(Text data mining). Showing records 1 – 30 of 203 total matches.

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Cornell University

1. Haque, Asif-ul. Information And Social System Interaction .

Degree: 2011, Cornell University

 Ever increasing participation has made the interaction between information and social systems not only interesting to observe but essential to quantify and analyze. This dissertation… (more)

Subjects/Keywords: data mining; networks; text mining

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APA (6th Edition):

Haque, A. (2011). Information And Social System Interaction . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/30752

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

Chicago Manual of Style (16th Edition):

Haque, Asif-ul. “Information And Social System Interaction .” 2011. Thesis, Cornell University. Accessed August 04, 2020. http://hdl.handle.net/1813/30752.

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

MLA Handbook (7th Edition):

Haque, Asif-ul. “Information And Social System Interaction .” 2011. Web. 04 Aug 2020.

Vancouver:

Haque A. Information And Social System Interaction . [Internet] [Thesis]. Cornell University; 2011. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/1813/30752.

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

Council of Science Editors:

Haque A. Information And Social System Interaction . [Thesis]. Cornell University; 2011. Available from: http://hdl.handle.net/1813/30752

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


Queens University

2. Lamb, Carolyn. Detecting Deception in Interrogation Settings .

Degree: Computing, 2012, Queens University

 Bag-of-words deception detection systems outperform humans, but are still not always accurate enough to be useful. In interrogation settings, present models do not take into… (more)

Subjects/Keywords: Data Mining ; Deception ; Text Mining

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APA (6th Edition):

Lamb, C. (2012). Detecting Deception in Interrogation Settings . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/7695

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

Chicago Manual of Style (16th Edition):

Lamb, Carolyn. “Detecting Deception in Interrogation Settings .” 2012. Thesis, Queens University. Accessed August 04, 2020. http://hdl.handle.net/1974/7695.

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

MLA Handbook (7th Edition):

Lamb, Carolyn. “Detecting Deception in Interrogation Settings .” 2012. Web. 04 Aug 2020.

Vancouver:

Lamb C. Detecting Deception in Interrogation Settings . [Internet] [Thesis]. Queens University; 2012. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/1974/7695.

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

Council of Science Editors:

Lamb C. Detecting Deception in Interrogation Settings . [Thesis]. Queens University; 2012. Available from: http://hdl.handle.net/1974/7695

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


Penn State University

3. Hu, Xiaocheng. DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH.

Degree: MS, Industrial Engineering, 2015, Penn State University

 Most of the researches on corporate filling mainly focus on qualitative analysis. This thesis used quantitative method-Bayesian Learning Machine in analyzing the information content of… (more)

Subjects/Keywords: data mining; text analysis; Perl

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APA (6th Edition):

Hu, X. (2015). DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/25731

Chicago Manual of Style (16th Edition):

Hu, Xiaocheng. “DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH.” 2015. Masters Thesis, Penn State University. Accessed August 04, 2020. https://etda.libraries.psu.edu/catalog/25731.

MLA Handbook (7th Edition):

Hu, Xiaocheng. “DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH.” 2015. Web. 04 Aug 2020.

Vancouver:

Hu X. DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH. [Internet] [Masters thesis]. Penn State University; 2015. [cited 2020 Aug 04]. Available from: https://etda.libraries.psu.edu/catalog/25731.

Council of Science Editors:

Hu X. DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH. [Masters Thesis]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/25731


University of Aberdeen

4. Barawi, Mohamad Hardyman. Automatic topic labelling and opinion summarisation.

Degree: PhD, 2019, University of Aberdeen

 With the global increase in online tools such as online reviews and social media platforms, individuals all around the globe have changed their way of… (more)

Subjects/Keywords: 004; Text data mining; Probabilities

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APA (6th Edition):

Barawi, M. H. (2019). Automatic topic labelling and opinion summarisation. (Doctoral Dissertation). University of Aberdeen. Retrieved from http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=243071 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105

Chicago Manual of Style (16th Edition):

Barawi, Mohamad Hardyman. “Automatic topic labelling and opinion summarisation.” 2019. Doctoral Dissertation, University of Aberdeen. Accessed August 04, 2020. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=243071 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105.

MLA Handbook (7th Edition):

Barawi, Mohamad Hardyman. “Automatic topic labelling and opinion summarisation.” 2019. Web. 04 Aug 2020.

Vancouver:

Barawi MH. Automatic topic labelling and opinion summarisation. [Internet] [Doctoral dissertation]. University of Aberdeen; 2019. [cited 2020 Aug 04]. Available from: http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=243071 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105.

Council of Science Editors:

Barawi MH. Automatic topic labelling and opinion summarisation. [Doctoral Dissertation]. University of Aberdeen; 2019. Available from: http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=243071 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105


University of Illinois – Urbana-Champaign

5. Tao, Fangbo. Text cube: construction, summarization and mining.

Degree: PhD, Computer Science, 2017, University of Illinois – Urbana-Champaign

 A large portion of real world data is either text or structured (\eg, relational) data. Such data objects are often linked together (\eg, structured product… (more)

Subjects/Keywords: Text cube; Data cube; Data mining; Natural language processing; Text classification

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APA (6th Edition):

Tao, F. (2017). Text cube: construction, summarization and mining. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99244

Chicago Manual of Style (16th Edition):

Tao, Fangbo. “Text cube: construction, summarization and mining.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed August 04, 2020. http://hdl.handle.net/2142/99244.

MLA Handbook (7th Edition):

Tao, Fangbo. “Text cube: construction, summarization and mining.” 2017. Web. 04 Aug 2020.

Vancouver:

Tao F. Text cube: construction, summarization and mining. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/2142/99244.

Council of Science Editors:

Tao F. Text cube: construction, summarization and mining. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99244


Queensland University of Technology

6. Albathan, Mubarak Murdi M. Enhancement of relevant features for text mining.

Degree: 2015, Queensland University of Technology

 With the explosion of information resources, there is an imminent need to understand interesting text features or topics in massive text information. This thesis proposes… (more)

Subjects/Keywords: Text Mining; Feature Selection; Information retrieval; Data Mining; pattern mining

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APA (6th Edition):

Albathan, M. M. M. (2015). Enhancement of relevant features for text mining. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/90072/

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

Chicago Manual of Style (16th Edition):

Albathan, Mubarak Murdi M. “Enhancement of relevant features for text mining.” 2015. Thesis, Queensland University of Technology. Accessed August 04, 2020. https://eprints.qut.edu.au/90072/.

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

MLA Handbook (7th Edition):

Albathan, Mubarak Murdi M. “Enhancement of relevant features for text mining.” 2015. Web. 04 Aug 2020.

Vancouver:

Albathan MMM. Enhancement of relevant features for text mining. [Internet] [Thesis]. Queensland University of Technology; 2015. [cited 2020 Aug 04]. Available from: https://eprints.qut.edu.au/90072/.

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

Council of Science Editors:

Albathan MMM. Enhancement of relevant features for text mining. [Thesis]. Queensland University of Technology; 2015. Available from: https://eprints.qut.edu.au/90072/

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


University of Arizona

7. Liu, Xiao. Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance .

Degree: 2016, University of Arizona

 Pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding, and prevention of adverse drug events (WHO 2004). Post-approval adverse drug… (more)

Subjects/Keywords: Health Data Analytics; Pharmacovigilance; Text Mining; Management; Data Mining

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APA (6th Edition):

Liu, X. (2016). Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/620913

Chicago Manual of Style (16th Edition):

Liu, Xiao. “Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance .” 2016. Doctoral Dissertation, University of Arizona. Accessed August 04, 2020. http://hdl.handle.net/10150/620913.

MLA Handbook (7th Edition):

Liu, Xiao. “Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance .” 2016. Web. 04 Aug 2020.

Vancouver:

Liu X. Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance . [Internet] [Doctoral dissertation]. University of Arizona; 2016. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/10150/620913.

Council of Science Editors:

Liu X. Health Data Analytics: Data and Text Mining Approaches for Pharmacovigilance . [Doctoral Dissertation]. University of Arizona; 2016. Available from: http://hdl.handle.net/10150/620913


University of Windsor

8. Ejieh, Chukwuma. ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS.

Degree: MS, Computer Science, 2016, University of Windsor

 Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the… (more)

Subjects/Keywords: Aspect-based Opinion Mining; Data Mining; Machine Learning; Microblogs; Opinion Mining; Text Mining

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APA (6th Edition):

Ejieh, C. (2016). ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/5728

Chicago Manual of Style (16th Edition):

Ejieh, Chukwuma. “ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS.” 2016. Masters Thesis, University of Windsor. Accessed August 04, 2020. https://scholar.uwindsor.ca/etd/5728.

MLA Handbook (7th Edition):

Ejieh, Chukwuma. “ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS.” 2016. Web. 04 Aug 2020.

Vancouver:

Ejieh C. ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS. [Internet] [Masters thesis]. University of Windsor; 2016. [cited 2020 Aug 04]. Available from: https://scholar.uwindsor.ca/etd/5728.

Council of Science Editors:

Ejieh C. ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS. [Masters Thesis]. University of Windsor; 2016. Available from: https://scholar.uwindsor.ca/etd/5728


Penn State University

9. Salaka, Vamsi. INFORMATION-THEORETIC APPROACHES FOR COMBINED MODELING OF QUALITATIVE AND QUANTITATIVE DATA.

Degree: PhD, Industrial Engineering, 2008, Penn State University

 In the past decade, remarkable progress has been made in developing data mining techniques limited to analyzing quantitative data along with text mining techniques limited… (more)

Subjects/Keywords: ontology; Data Mining; Text Mining; Information theory; vector space modeling

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APA (6th Edition):

Salaka, V. (2008). INFORMATION-THEORETIC APPROACHES FOR COMBINED MODELING OF QUALITATIVE AND QUANTITATIVE DATA. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/8465

Chicago Manual of Style (16th Edition):

Salaka, Vamsi. “INFORMATION-THEORETIC APPROACHES FOR COMBINED MODELING OF QUALITATIVE AND QUANTITATIVE DATA.” 2008. Doctoral Dissertation, Penn State University. Accessed August 04, 2020. https://etda.libraries.psu.edu/catalog/8465.

MLA Handbook (7th Edition):

Salaka, Vamsi. “INFORMATION-THEORETIC APPROACHES FOR COMBINED MODELING OF QUALITATIVE AND QUANTITATIVE DATA.” 2008. Web. 04 Aug 2020.

Vancouver:

Salaka V. INFORMATION-THEORETIC APPROACHES FOR COMBINED MODELING OF QUALITATIVE AND QUANTITATIVE DATA. [Internet] [Doctoral dissertation]. Penn State University; 2008. [cited 2020 Aug 04]. Available from: https://etda.libraries.psu.edu/catalog/8465.

Council of Science Editors:

Salaka V. INFORMATION-THEORETIC APPROACHES FOR COMBINED MODELING OF QUALITATIVE AND QUANTITATIVE DATA. [Doctoral Dissertation]. Penn State University; 2008. Available from: https://etda.libraries.psu.edu/catalog/8465


University of Alberta

10. Hasan, Maryam. Extracting Structured Knowledge from Textual Data in Software Repositories.

Degree: MS, Computing Science, 2011, University of Alberta

 Software team members, as they communicate and coordinate their work with others throughout the life-cycle of their projects, generate different kinds of textual artifacts. Despite… (more)

Subjects/Keywords: Mining Software Repositories, Textual Data, Text Mining, Knowledge Extraction

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APA (6th Edition):

Hasan, M. (2011). Extracting Structured Knowledge from Textual Data in Software Repositories. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cf95jb62q

Chicago Manual of Style (16th Edition):

Hasan, Maryam. “Extracting Structured Knowledge from Textual Data in Software Repositories.” 2011. Masters Thesis, University of Alberta. Accessed August 04, 2020. https://era.library.ualberta.ca/files/cf95jb62q.

MLA Handbook (7th Edition):

Hasan, Maryam. “Extracting Structured Knowledge from Textual Data in Software Repositories.” 2011. Web. 04 Aug 2020.

Vancouver:

Hasan M. Extracting Structured Knowledge from Textual Data in Software Repositories. [Internet] [Masters thesis]. University of Alberta; 2011. [cited 2020 Aug 04]. Available from: https://era.library.ualberta.ca/files/cf95jb62q.

Council of Science Editors:

Hasan M. Extracting Structured Knowledge from Textual Data in Software Repositories. [Masters Thesis]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/cf95jb62q


University of Louisville

11. Tang, Guoxin. Data mining and analysis of lung cancer data.

Degree: PhD, 2010, University of Louisville

  Lung cancer is the leading cause of cancer death in the United States and the world, with more than 1.3 million deaths worldwide per… (more)

Subjects/Keywords: Data mining; Text mining; Lung cancer; Health care; Predictive modeling

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APA (6th Edition):

Tang, G. (2010). Data mining and analysis of lung cancer data. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/1418 ; https://ir.library.louisville.edu/etd/1418

Chicago Manual of Style (16th Edition):

Tang, Guoxin. “Data mining and analysis of lung cancer data.” 2010. Doctoral Dissertation, University of Louisville. Accessed August 04, 2020. 10.18297/etd/1418 ; https://ir.library.louisville.edu/etd/1418.

MLA Handbook (7th Edition):

Tang, Guoxin. “Data mining and analysis of lung cancer data.” 2010. Web. 04 Aug 2020.

Vancouver:

Tang G. Data mining and analysis of lung cancer data. [Internet] [Doctoral dissertation]. University of Louisville; 2010. [cited 2020 Aug 04]. Available from: 10.18297/etd/1418 ; https://ir.library.louisville.edu/etd/1418.

Council of Science Editors:

Tang G. Data mining and analysis of lung cancer data. [Doctoral Dissertation]. University of Louisville; 2010. Available from: 10.18297/etd/1418 ; https://ir.library.louisville.edu/etd/1418

12. Koteeswaran, S. Meta data conceptual mining model using analysis of bilateral intelligence for effective text clustering; -.

Degree: Engineering, 2014, Vel Tech Dr. R R and Dr. S R Technical University

Data mining from organizational data for extracting hidden knowledge is a growing field of study, which forms new study groups named knowledge discovery in database.… (more)

Subjects/Keywords: Computer Science; Data Mining; Text Mining; Naïve; Levenberg Marquardt algorithm

Page 1

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APA (6th Edition):

Koteeswaran, S. (2014). Meta data conceptual mining model using analysis of bilateral intelligence for effective text clustering; -. (Thesis). Vel Tech Dr. R R and Dr. S R Technical University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/16044

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

Chicago Manual of Style (16th Edition):

Koteeswaran, S. “Meta data conceptual mining model using analysis of bilateral intelligence for effective text clustering; -.” 2014. Thesis, Vel Tech Dr. R R and Dr. S R Technical University. Accessed August 04, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/16044.

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

MLA Handbook (7th Edition):

Koteeswaran, S. “Meta data conceptual mining model using analysis of bilateral intelligence for effective text clustering; -.” 2014. Web. 04 Aug 2020.

Vancouver:

Koteeswaran S. Meta data conceptual mining model using analysis of bilateral intelligence for effective text clustering; -. [Internet] [Thesis]. Vel Tech Dr. R R and Dr. S R Technical University; 2014. [cited 2020 Aug 04]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/16044.

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

Council of Science Editors:

Koteeswaran S. Meta data conceptual mining model using analysis of bilateral intelligence for effective text clustering; -. [Thesis]. Vel Tech Dr. R R and Dr. S R Technical University; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/16044

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


University of Michigan

13. Chen, Zhe. Information Extraction on Para-Relational Data.

Degree: PhD, Computer Science and Engineering, 2016, University of Michigan

 Para-relational data (such as spreadsheets and diagrams) refers to a type of nearly relational data that shares the important qualities of relational data but does… (more)

Subjects/Keywords: information extraction; data mining; text mining; Computer Science; Engineering

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APA (6th Edition):

Chen, Z. (2016). Information Extraction on Para-Relational Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/120853

Chicago Manual of Style (16th Edition):

Chen, Zhe. “Information Extraction on Para-Relational Data.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 04, 2020. http://hdl.handle.net/2027.42/120853.

MLA Handbook (7th Edition):

Chen, Zhe. “Information Extraction on Para-Relational Data.” 2016. Web. 04 Aug 2020.

Vancouver:

Chen Z. Information Extraction on Para-Relational Data. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/2027.42/120853.

Council of Science Editors:

Chen Z. Information Extraction on Para-Relational Data. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/120853


University of Arizona

14. Xie, Jiaheng. Big Data-Based Health Risk Analytics: A Deep Learning Approach .

Degree: 2020, University of Arizona

 Value-based healthcare is an emerging healthcare delivery model which incentivizes and rewards physicians for improved patient outcomes and quality of care, rather than the amount… (more)

Subjects/Keywords: Data mining; Deep learning; Design science; Health risk analytics; Text mining

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APA (6th Edition):

Xie, J. (2020). Big Data-Based Health Risk Analytics: A Deep Learning Approach . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/641367

Chicago Manual of Style (16th Edition):

Xie, Jiaheng. “Big Data-Based Health Risk Analytics: A Deep Learning Approach .” 2020. Doctoral Dissertation, University of Arizona. Accessed August 04, 2020. http://hdl.handle.net/10150/641367.

MLA Handbook (7th Edition):

Xie, Jiaheng. “Big Data-Based Health Risk Analytics: A Deep Learning Approach .” 2020. Web. 04 Aug 2020.

Vancouver:

Xie J. Big Data-Based Health Risk Analytics: A Deep Learning Approach . [Internet] [Doctoral dissertation]. University of Arizona; 2020. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/10150/641367.

Council of Science Editors:

Xie J. Big Data-Based Health Risk Analytics: A Deep Learning Approach . [Doctoral Dissertation]. University of Arizona; 2020. Available from: http://hdl.handle.net/10150/641367


Iowa State University

15. Yang, Xiaoli. A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database.

Degree: 2011, Iowa State University

Mining is one of the most dangerous industries. Mine Safety and Health Administration (MSHA) maintains a database that records thousands of mining related accidents, injuries… (more)

Subjects/Keywords: Bag-of-Words; Clustering; Data Mining; Mining Safety; Text Mining; Industrial Engineering

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APA (6th Edition):

Yang, X. (2011). A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/12074

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

Chicago Manual of Style (16th Edition):

Yang, Xiaoli. “A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database.” 2011. Thesis, Iowa State University. Accessed August 04, 2020. https://lib.dr.iastate.edu/etd/12074.

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

MLA Handbook (7th Edition):

Yang, Xiaoli. “A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database.” 2011. Web. 04 Aug 2020.

Vancouver:

Yang X. A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database. [Internet] [Thesis]. Iowa State University; 2011. [cited 2020 Aug 04]. Available from: https://lib.dr.iastate.edu/etd/12074.

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

Council of Science Editors:

Yang X. A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database. [Thesis]. Iowa State University; 2011. Available from: https://lib.dr.iastate.edu/etd/12074

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


Georgia Tech

16. Uppal, Karan. Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization.

Degree: PhD, Biology, 2015, Georgia Tech

 This thesis focuses on developing a computational framework to support the Precision Medicine Initiative. The newly developed tools and algorithms use machine learning, text mining(more)

Subjects/Keywords: Literature mining; Literature-based discovery; Text mining; Text summarization; Visualization; Content recognition; Feature selection; Data mining

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

APA (6th Edition):

Uppal, K. (2015). Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55548

Chicago Manual of Style (16th Edition):

Uppal, Karan. “Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization.” 2015. Doctoral Dissertation, Georgia Tech. Accessed August 04, 2020. http://hdl.handle.net/1853/55548.

MLA Handbook (7th Edition):

Uppal, Karan. “Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization.” 2015. Web. 04 Aug 2020.

Vancouver:

Uppal K. Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/1853/55548.

Council of Science Editors:

Uppal K. Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/55548


Arizona State University

17. Swadia, Japa Nimish. A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems.

Degree: Engineering, 2016, Arizona State University

Text Classification is a rapidly evolving area of Data Mining while Requirements Engineering is a less-explored area of Software Engineering which deals the process of… (more)

Subjects/Keywords: Computer science; Engineering; data analytics; R; requirements classification; text classification; text mining

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

APA (6th Edition):

Swadia, J. N. (2016). A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/38809

Chicago Manual of Style (16th Edition):

Swadia, Japa Nimish. “A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems.” 2016. Masters Thesis, Arizona State University. Accessed August 04, 2020. http://repository.asu.edu/items/38809.

MLA Handbook (7th Edition):

Swadia, Japa Nimish. “A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems.” 2016. Web. 04 Aug 2020.

Vancouver:

Swadia JN. A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems. [Internet] [Masters thesis]. Arizona State University; 2016. [cited 2020 Aug 04]. Available from: http://repository.asu.edu/items/38809.

Council of Science Editors:

Swadia JN. A Study of Text Mining Framework for Automated Classification of Software Requirements in Enterprise Systems. [Masters Thesis]. Arizona State University; 2016. Available from: http://repository.asu.edu/items/38809


Brno University of Technology

18. Harár, Pavol. Zlepšení předpovědi sociálních značek využitím Data Mining: Improved Prediction of Social Tags Using Data Mining.

Degree: 2019, Brno University of Technology

 This master’s thesis deals with using Text mining as a method to predict tags of articles. It describes the iterative way of handling big data(more)

Subjects/Keywords: Text mining; Data mining; TF-IDF; iteratívny rozbor; skóring; značky; Python; Text mining; Data mining; TF-IDF; iterative parsing; scoring; tags; Python

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

APA (6th Edition):

Harár, P. (2019). Zlepšení předpovědi sociálních značek využitím Data Mining: Improved Prediction of Social Tags Using Data Mining. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/39521

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

Chicago Manual of Style (16th Edition):

Harár, Pavol. “Zlepšení předpovědi sociálních značek využitím Data Mining: Improved Prediction of Social Tags Using Data Mining.” 2019. Thesis, Brno University of Technology. Accessed August 04, 2020. http://hdl.handle.net/11012/39521.

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

MLA Handbook (7th Edition):

Harár, Pavol. “Zlepšení předpovědi sociálních značek využitím Data Mining: Improved Prediction of Social Tags Using Data Mining.” 2019. Web. 04 Aug 2020.

Vancouver:

Harár P. Zlepšení předpovědi sociálních značek využitím Data Mining: Improved Prediction of Social Tags Using Data Mining. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/11012/39521.

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

Council of Science Editors:

Harár P. Zlepšení předpovědi sociálních značek využitím Data Mining: Improved Prediction of Social Tags Using Data Mining. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/39521

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

19. Milosevic, Nikola Ljubisa. A multi-layered approach to information extraction from tables in biomedical documents.

Degree: 2018, University of Manchester

The quantity of literature in the biomedical domain is growing exponentially. It is becoming impossible for researchers to cope with this ever-increasing amount of information.… (more)

Subjects/Keywords: table mining; information extraction; text mining; natural language processing; literature mining; health informatics; data curation; machine learning; data annotation

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

APA (6th Edition):

Milosevic, N. L. (2018). A multi-layered approach to information extraction from tables in biomedical documents. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314635

Chicago Manual of Style (16th Edition):

Milosevic, Nikola Ljubisa. “A multi-layered approach to information extraction from tables in biomedical documents.” 2018. Doctoral Dissertation, University of Manchester. Accessed August 04, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314635.

MLA Handbook (7th Edition):

Milosevic, Nikola Ljubisa. “A multi-layered approach to information extraction from tables in biomedical documents.” 2018. Web. 04 Aug 2020.

Vancouver:

Milosevic NL. A multi-layered approach to information extraction from tables in biomedical documents. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2020 Aug 04]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314635.

Council of Science Editors:

Milosevic NL. A multi-layered approach to information extraction from tables in biomedical documents. [Doctoral Dissertation]. University of Manchester; 2018. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:314635


University of Manchester

20. Milosevic, Nikola. A multi-layered approach to information extraction from tables in biomedical documents.

Degree: PhD, 2018, University of Manchester

 The quantity of literature in the biomedical domain is growing exponentially. It is becoming impossible for researchers to cope with this ever-increasing amount of information.… (more)

Subjects/Keywords: 004; health informatics; machine learning; data curation; literature mining; data annotation; text mining; information extraction; table mining; natural language processing

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

APA (6th Edition):

Milosevic, N. (2018). A multi-layered approach to information extraction from tables in biomedical documents. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/a-multilayered-approach-to-information-extraction-from-tables-in-biomedical-documents(c2edce9c-ae7f-48fa-81c2-14d4bb87423e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748048

Chicago Manual of Style (16th Edition):

Milosevic, Nikola. “A multi-layered approach to information extraction from tables in biomedical documents.” 2018. Doctoral Dissertation, University of Manchester. Accessed August 04, 2020. https://www.research.manchester.ac.uk/portal/en/theses/a-multilayered-approach-to-information-extraction-from-tables-in-biomedical-documents(c2edce9c-ae7f-48fa-81c2-14d4bb87423e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748048.

MLA Handbook (7th Edition):

Milosevic, Nikola. “A multi-layered approach to information extraction from tables in biomedical documents.” 2018. Web. 04 Aug 2020.

Vancouver:

Milosevic N. A multi-layered approach to information extraction from tables in biomedical documents. [Internet] [Doctoral dissertation]. University of Manchester; 2018. [cited 2020 Aug 04]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/a-multilayered-approach-to-information-extraction-from-tables-in-biomedical-documents(c2edce9c-ae7f-48fa-81c2-14d4bb87423e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748048.

Council of Science Editors:

Milosevic N. A multi-layered approach to information extraction from tables in biomedical documents. [Doctoral Dissertation]. University of Manchester; 2018. Available from: https://www.research.manchester.ac.uk/portal/en/theses/a-multilayered-approach-to-information-extraction-from-tables-in-biomedical-documents(c2edce9c-ae7f-48fa-81c2-14d4bb87423e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748048


University of Illinois – Urbana-Champaign

21. Wang, Chi. Mining latent entity structures from massive unstructured and interconnected data.

Degree: PhD, 0112, 2015, University of Illinois – Urbana-Champaign

 The “big data” era is characterized by an explosion of information in the form of digital data collections, ranging from scientific knowledge, to social media,… (more)

Subjects/Keywords: data mining; text mining; information network; social network; network analysis; probabilistic graphical model; topic model; phrase mining; relation mining; Information Extraction

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

APA (6th Edition):

Wang, C. (2015). Mining latent entity structures from massive unstructured and interconnected data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72967

Chicago Manual of Style (16th Edition):

Wang, Chi. “Mining latent entity structures from massive unstructured and interconnected data.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed August 04, 2020. http://hdl.handle.net/2142/72967.

MLA Handbook (7th Edition):

Wang, Chi. “Mining latent entity structures from massive unstructured and interconnected data.” 2015. Web. 04 Aug 2020.

Vancouver:

Wang C. Mining latent entity structures from massive unstructured and interconnected data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/2142/72967.

Council of Science Editors:

Wang C. Mining latent entity structures from massive unstructured and interconnected data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72967


University of Utah

22. Kim, Iljoo. Predicting audience demographics of web sites using local cues.

Degree: PhD, Business Administration, 2011, University of Utah

 The size and dynamism of the Web poses challenges for all its stakeholders, which include producers/consumers of content, and advertisers who want to place advertisements… (more)

Subjects/Keywords: Audience demographics; Data mining; Online marketing strategies; Predictive modeling; text; classification; Web mining

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

APA (6th Edition):

Kim, I. (2011). Predicting audience demographics of web sites using local cues. (Doctoral Dissertation). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/91/rec/1936

Chicago Manual of Style (16th Edition):

Kim, Iljoo. “Predicting audience demographics of web sites using local cues.” 2011. Doctoral Dissertation, University of Utah. Accessed August 04, 2020. http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/91/rec/1936.

MLA Handbook (7th Edition):

Kim, Iljoo. “Predicting audience demographics of web sites using local cues.” 2011. Web. 04 Aug 2020.

Vancouver:

Kim I. Predicting audience demographics of web sites using local cues. [Internet] [Doctoral dissertation]. University of Utah; 2011. [cited 2020 Aug 04]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/91/rec/1936.

Council of Science Editors:

Kim I. Predicting audience demographics of web sites using local cues. [Doctoral Dissertation]. University of Utah; 2011. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/91/rec/1936


University of Georgia

23. Desai, Sanmit Tatoba. SMART Sentiment and Emotion Analysis.

Degree: MS, Computer Science, 2014, University of Georgia

 With the rising social conflicts in different parts of the world the need to understand the feelings and opinions of the general populous is also… (more)

Subjects/Keywords: Emotions Analysis; Sentiment Analysis; NLP; Information Gathering; Data Mining; Text Mining; DBpedia

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

APA (6th Edition):

Desai, S. T. (2014). SMART Sentiment and Emotion Analysis. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/desai_sanmit_t_201412_ms

Chicago Manual of Style (16th Edition):

Desai, Sanmit Tatoba. “SMART Sentiment and Emotion Analysis.” 2014. Masters Thesis, University of Georgia. Accessed August 04, 2020. http://purl.galileo.usg.edu/uga_etd/desai_sanmit_t_201412_ms.

MLA Handbook (7th Edition):

Desai, Sanmit Tatoba. “SMART Sentiment and Emotion Analysis.” 2014. Web. 04 Aug 2020.

Vancouver:

Desai ST. SMART Sentiment and Emotion Analysis. [Internet] [Masters thesis]. University of Georgia; 2014. [cited 2020 Aug 04]. Available from: http://purl.galileo.usg.edu/uga_etd/desai_sanmit_t_201412_ms.

Council of Science Editors:

Desai ST. SMART Sentiment and Emotion Analysis. [Masters Thesis]. University of Georgia; 2014. Available from: http://purl.galileo.usg.edu/uga_etd/desai_sanmit_t_201412_ms


Penn State University

24. Qiu, Baojun. SOCIAL NETWORK MODELING, LINK PREDICTION, AND SENTIMENT IMPACT ANALYSIS.

Degree: PhD, Computer Science, 2011, Penn State University

 Social network dynamics analysis is one of the most important fields in social network analysis. It studies the temporal network structure and impacts on actors… (more)

Subjects/Keywords: temporal data mining; machine learning; Social network analysis; text mining; sentiment analysis; link prediction

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APA (6th Edition):

Qiu, B. (2011). SOCIAL NETWORK MODELING, LINK PREDICTION, AND SENTIMENT IMPACT ANALYSIS. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/12409

Chicago Manual of Style (16th Edition):

Qiu, Baojun. “SOCIAL NETWORK MODELING, LINK PREDICTION, AND SENTIMENT IMPACT ANALYSIS.” 2011. Doctoral Dissertation, Penn State University. Accessed August 04, 2020. https://etda.libraries.psu.edu/catalog/12409.

MLA Handbook (7th Edition):

Qiu, Baojun. “SOCIAL NETWORK MODELING, LINK PREDICTION, AND SENTIMENT IMPACT ANALYSIS.” 2011. Web. 04 Aug 2020.

Vancouver:

Qiu B. SOCIAL NETWORK MODELING, LINK PREDICTION, AND SENTIMENT IMPACT ANALYSIS. [Internet] [Doctoral dissertation]. Penn State University; 2011. [cited 2020 Aug 04]. Available from: https://etda.libraries.psu.edu/catalog/12409.

Council of Science Editors:

Qiu B. SOCIAL NETWORK MODELING, LINK PREDICTION, AND SENTIMENT IMPACT ANALYSIS. [Doctoral Dissertation]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/12409


University of California – Merced

25. Farhadloo, Mohsen. Statistical Models for Aspect-Level Sentiment Analysis.

Degree: Electrical Engineering and Computer Science, 2015, University of California – Merced

 Sentiment analysis and opinion mining is the field of computational study of people’sopinion expressed in written language or text. Sentiment analysis brings together variousresearch areas… (more)

Subjects/Keywords: Engineering; Computer science; Bayesian Statistics; Data Mining; Machine Learning; Sentiment Analysis; Statistical Models; Text Mining

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APA (6th Edition):

Farhadloo, M. (2015). Statistical Models for Aspect-Level Sentiment Analysis. (Thesis). University of California – Merced. Retrieved from http://www.escholarship.org/uc/item/2ks913br

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

Chicago Manual of Style (16th Edition):

Farhadloo, Mohsen. “Statistical Models for Aspect-Level Sentiment Analysis.” 2015. Thesis, University of California – Merced. Accessed August 04, 2020. http://www.escholarship.org/uc/item/2ks913br.

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

MLA Handbook (7th Edition):

Farhadloo, Mohsen. “Statistical Models for Aspect-Level Sentiment Analysis.” 2015. Web. 04 Aug 2020.

Vancouver:

Farhadloo M. Statistical Models for Aspect-Level Sentiment Analysis. [Internet] [Thesis]. University of California – Merced; 2015. [cited 2020 Aug 04]. Available from: http://www.escholarship.org/uc/item/2ks913br.

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

Council of Science Editors:

Farhadloo M. Statistical Models for Aspect-Level Sentiment Analysis. [Thesis]. University of California – Merced; 2015. Available from: http://www.escholarship.org/uc/item/2ks913br

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


University of Wisconsin – Milwaukee

26. Towhidi, Gelareh. Three Essays on Trust Mining in Online Social Networks.

Degree: PhD, Information Technology Management, 2018, University of Wisconsin – Milwaukee

  This dissertation research consists of three essays on studying trust in online social networks. Trust plays a critical role in online social relationships, because… (more)

Subjects/Keywords: Data Mining; Online Trust; Social Network; Text Mining; Databases and Information Systems; Human Resources Management

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

APA (6th Edition):

Towhidi, G. (2018). Three Essays on Trust Mining in Online Social Networks. (Doctoral Dissertation). University of Wisconsin – Milwaukee. Retrieved from https://dc.uwm.edu/etd/1932

Chicago Manual of Style (16th Edition):

Towhidi, Gelareh. “Three Essays on Trust Mining in Online Social Networks.” 2018. Doctoral Dissertation, University of Wisconsin – Milwaukee. Accessed August 04, 2020. https://dc.uwm.edu/etd/1932.

MLA Handbook (7th Edition):

Towhidi, Gelareh. “Three Essays on Trust Mining in Online Social Networks.” 2018. Web. 04 Aug 2020.

Vancouver:

Towhidi G. Three Essays on Trust Mining in Online Social Networks. [Internet] [Doctoral dissertation]. University of Wisconsin – Milwaukee; 2018. [cited 2020 Aug 04]. Available from: https://dc.uwm.edu/etd/1932.

Council of Science Editors:

Towhidi G. Three Essays on Trust Mining in Online Social Networks. [Doctoral Dissertation]. University of Wisconsin – Milwaukee; 2018. Available from: https://dc.uwm.edu/etd/1932

27. Pereira, Luís Miguel Oliveira. Utilização de técnicas de text mining sobre registos clínicos de epilepsia em crianças, para auxílio ao diagnóstico e classificação.

Degree: 2013, Instituto Politécnico de Leiria

Dissertação apresentado à Escola Superior de Tecnologia e Gestão do IPL para obtenção do grau de Mestre em Engenharia Informática - Computação Móvel, orientada pelo… (more)

Subjects/Keywords: Sistemas de suporte à decisão; Epilepsia; Registos clínicos; Códigos ICD-9; Text mining; Data mining

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APA (6th Edition):

Pereira, L. M. O. (2013). Utilização de técnicas de text mining sobre registos clínicos de epilepsia em crianças, para auxílio ao diagnóstico e classificação. (Thesis). Instituto Politécnico de Leiria. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/1349

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

Chicago Manual of Style (16th Edition):

Pereira, Luís Miguel Oliveira. “Utilização de técnicas de text mining sobre registos clínicos de epilepsia em crianças, para auxílio ao diagnóstico e classificação.” 2013. Thesis, Instituto Politécnico de Leiria. Accessed August 04, 2020. https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/1349.

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

MLA Handbook (7th Edition):

Pereira, Luís Miguel Oliveira. “Utilização de técnicas de text mining sobre registos clínicos de epilepsia em crianças, para auxílio ao diagnóstico e classificação.” 2013. Web. 04 Aug 2020.

Vancouver:

Pereira LMO. Utilização de técnicas de text mining sobre registos clínicos de epilepsia em crianças, para auxílio ao diagnóstico e classificação. [Internet] [Thesis]. Instituto Politécnico de Leiria; 2013. [cited 2020 Aug 04]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/1349.

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

Council of Science Editors:

Pereira LMO. Utilização de técnicas de text mining sobre registos clínicos de epilepsia em crianças, para auxílio ao diagnóstico e classificação. [Thesis]. Instituto Politécnico de Leiria; 2013. Available from: https://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/1349

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


University of Cincinnati

28. Ghanem, Amer G. Identifying Patterns of Epistemic Organization through Network-Based Analysis of Text Corpora.

Degree: PhD, Engineering and Applied Science: Computer Science and Engineering, 2015, University of Cincinnati

 The growth of on-line textual content has exploded in recent years, creating truly massive text corpora. As the quantity of text available on- line increases,… (more)

Subjects/Keywords: Computer Science; Data Mining; Text Mining; Topic Extraction; Semantic Analysis; Community Extraction; Semantic Spaces

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

APA (6th Edition):

Ghanem, A. G. (2015). Identifying Patterns of Epistemic Organization through Network-Based Analysis of Text Corpora. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448274706

Chicago Manual of Style (16th Edition):

Ghanem, Amer G. “Identifying Patterns of Epistemic Organization through Network-Based Analysis of Text Corpora.” 2015. Doctoral Dissertation, University of Cincinnati. Accessed August 04, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448274706.

MLA Handbook (7th Edition):

Ghanem, Amer G. “Identifying Patterns of Epistemic Organization through Network-Based Analysis of Text Corpora.” 2015. Web. 04 Aug 2020.

Vancouver:

Ghanem AG. Identifying Patterns of Epistemic Organization through Network-Based Analysis of Text Corpora. [Internet] [Doctoral dissertation]. University of Cincinnati; 2015. [cited 2020 Aug 04]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448274706.

Council of Science Editors:

Ghanem AG. Identifying Patterns of Epistemic Organization through Network-Based Analysis of Text Corpora. [Doctoral Dissertation]. University of Cincinnati; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448274706


University of Illinois – Chicago

29. Mukherjee, Arjun. Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications.

Degree: 2014, University of Illinois – Chicago

 Public sentiments in online debates, discussions, comments are crucial to governmental agencies for passing new bills/policy, gauging upheaval, predicting elections, etc. However, to leverage the… (more)

Subjects/Keywords: Data mining; Graphical models; Natural language processing; Social media; Statistics; Text mining.

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APA (6th Edition):

Mukherjee, A. (2014). Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/19083

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

Chicago Manual of Style (16th Edition):

Mukherjee, Arjun. “Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications.” 2014. Thesis, University of Illinois – Chicago. Accessed August 04, 2020. http://hdl.handle.net/10027/19083.

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

MLA Handbook (7th Edition):

Mukherjee, Arjun. “Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications.” 2014. Web. 04 Aug 2020.

Vancouver:

Mukherjee A. Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications. [Internet] [Thesis]. University of Illinois – Chicago; 2014. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/10027/19083.

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

Council of Science Editors:

Mukherjee A. Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications. [Thesis]. University of Illinois – Chicago; 2014. Available from: http://hdl.handle.net/10027/19083

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


Texas State University – San Marcos

30. Yazdizadeh Shotorbani, Peyman. Text Mining Techniques for Analyzing Unstructured Manufacturing Data.

Degree: MS, Technology Management, 2016, Texas State University – San Marcos

 Manufacturing companies are increasingly enhancing their web presence as a strategy for improving their visibility in the global market. The exponential growth of manufacturing websites… (more)

Subjects/Keywords: Text mining; Topic modeling; Classification; Clustering; Manufacturing; Machine learning; Supplier discovery; Data mining

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APA (6th Edition):

Yazdizadeh Shotorbani, P. (2016). Text Mining Techniques for Analyzing Unstructured Manufacturing Data. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6304

Chicago Manual of Style (16th Edition):

Yazdizadeh Shotorbani, Peyman. “Text Mining Techniques for Analyzing Unstructured Manufacturing Data.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed August 04, 2020. https://digital.library.txstate.edu/handle/10877/6304.

MLA Handbook (7th Edition):

Yazdizadeh Shotorbani, Peyman. “Text Mining Techniques for Analyzing Unstructured Manufacturing Data.” 2016. Web. 04 Aug 2020.

Vancouver:

Yazdizadeh Shotorbani P. Text Mining Techniques for Analyzing Unstructured Manufacturing Data. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Aug 04]. Available from: https://digital.library.txstate.edu/handle/10877/6304.

Council of Science Editors:

Yazdizadeh Shotorbani P. Text Mining Techniques for Analyzing Unstructured Manufacturing Data. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6304

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