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Optimal Semantic Labeling of Social Network Clusters

Peng, Shuyue
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821134

Abstract Details

2014, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Twitter is one of the most popular social networking services. By analyzing social network data, meaningful information can be discovered, such as popular topics users are discussing and trends of important events. This thesis focuses on the problem of discovering and optimizing semantic labeling of network clusters using Twitter data sets. Specifically, we focus on prominent, individual Twitter accounts around the University of Cincinnati. With its heavily structured nature, Twitter is an appropriate environment in which to observe social interactions and determine the level of influence a given individual exerts on the people who see their content. The data sets we used in this thesis consists of a very active group of users associated with the account of President of University of Cincinnati. By applying an algorithmic design based on Order Statistics Local Optimization Method(OSLOM) algorithm, we cluster all accounts into several groupings or clusters according to their mutual relationships. We also develop a method to label accounts by analyzing tweets from selected accounts. We eliminate stop words and label accounts by word occurrence number to find out their interests and hot topics.?
Fred Annexstein, Ph.D. (Committee Chair)
Wen Ben Jone, Ph.D. (Committee Member)
Anca Ralescu, Ph.D. (Committee Member)
57 p.
Computer Science
clustering; social network; semantic label

Recommended Citations

Citations

  • Peng, S. (2014). Optimal Semantic Labeling of Social Network Clusters [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821134

    APA Style (7th edition)

  • Peng, Shuyue. Optimal Semantic Labeling of Social Network Clusters. 2014. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821134.

    MLA Style (8th edition)

  • Peng, Shuyue. "Optimal Semantic Labeling of Social Network Clusters." Master's thesis, University of Cincinnati, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406821134

    Chicago Manual of Style (17th edition)

ucin1406821134
722
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This open access ETD is published by University of Cincinnati and OhioLINK.