Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for subject:(hiring graph). One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


Texas A&M University

1. Huang, Bolun. Sentiment-based Classification of Tweeters and University Programs.

Degree: 2014, Texas A&M University

The rapidly growing World WideWeb (WWW) is no longer a passive information provider. Nowadays, Internet users themselves have become contributors to the WWW. A lot of user generated data, along with non-user-generated data, make our world an informative, however, perhaps over-informed society. The increasing amount of unorganized, disordered, unstructured, or even randomly generated data drove the momentum of big data analysis, aiming to discover and learn the hidden patterns behind the data. In this thesis, in particular, we look at two problems of mining knowledge from data. In the first project, we are trying to classify "democrats" and "republicans" in Twitter. We first propose a sentiment-based classification model to classify "democrats" and "republicans", with the aim to address the problem that conventional quantitative features, such as tweet count, follower-to-following ratio, election tweet count, cannot reflect the opinion alignment of tweeters. Therefore we utilize sentiment scores over multiple topics as our feature vector in the classification model. We innovatively proposed an automatic topic selection model to learn those distinguishing topics, making the sentiment feature selection domain independent. However, the sentiment-based classification model is not doing much better than non-sentiment model. Given the fact that sentiment-based classification model is not doing well enough, we propose using social relationship graph information to adjust our sentiment vectors. The graph-adjusted sentiment model achieves an accuracy higher than 80 percent in classification. What's more, we deploy a completely graph-based model, Belief Propagation (BP) model on the social graph, which achieves a prediction accuracy higher than 85 percent. We conclude that the effect of social relationship graph is more important than sentiment of tweets for classifying users into "democrats" and "republicans". In the second project, we propose an alternative and new way to rank graduate schools using algorithms, instead of using qualitative surveys as U.S. News does. Based on the assumption that "schools tend to hire PhD graduates from better or peer schools" to become their faculty members, we propose deploying link-based ranking algorithms on the "hiring graph" among universities. We refine PageRank (PR) algorithm and Hyperlink-induced Topic Search (HITS) Algorithm by taking the edge weight into consideration, as our own way to rank graduate programs. In order to validate our approach, we collect two separate data sets to construct the "hiring graph", faculty data in top 50 Computer Science (CS) programs and faculty data in top 50 Mechanical Engineering (ME) programs across the United States. By comparing our new rankings with U.S. News ranking, we discover that some programs are either under-ranked or over-ranked by U.S. News. We also conduct extensive data analysis on our data, revealing a lot of interesting patterns and cases behind the U.S. News ranking. Finally, we conduct sensitivity analysis on each proposed… Advisors/Committee Members: Reddy, Narasimha (advisor), Gu, Guofei (committee member), Shakkottai, Srinivas (committee member).

Subjects/Keywords: Sentiment; Classification; Twitter; University Ranking; Algorithm; hiring graph

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Huang, B. (2014). Sentiment-based Classification of Tweeters and University Programs. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153569

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):

Huang, Bolun. “Sentiment-based Classification of Tweeters and University Programs.” 2014. Thesis, Texas A&M University. Accessed October 14, 2019. http://hdl.handle.net/1969.1/153569.

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

MLA Handbook (7th Edition):

Huang, Bolun. “Sentiment-based Classification of Tweeters and University Programs.” 2014. Web. 14 Oct 2019.

Vancouver:

Huang B. Sentiment-based Classification of Tweeters and University Programs. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Oct 14]. Available from: http://hdl.handle.net/1969.1/153569.

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. Sentiment-based Classification of Tweeters and University Programs. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153569

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

.