Text classification and sentiment analysis in social networks using a probability model

Simple item page
dc.contributor.advisorKim, Yanggon
dc.contributor.authorLee, Hyeonchoel
dc.contributor.departmentTowson University. Department of Computer and Information Sciences
dc.date.accessioned2015-12-17T19:36:34Z
dc.date.available2015-12-17T19:36:34Z
dc.date.issued2015-09-04
dc.date.submitted2015-05
dc.description(D.Sc.) -- Towson University, 2015.
dc.description.abstractIn recent years, diverse social networks, such as Facebook, YouTube, and Twitter, have rapidly grown in size and influence, and a huge amount of data is being generated from the social networks in real time. Demands for data mining on social networks have been dramatically increasing, since analyzing the data can yield insights and understanding to real world phenomenon. However, there are a lot of challenges and difficulties with data collection, management, and analysis because of the features of the social networks' data: large, noisy, and dynamic. Therefore, this study will address the overall problems with data mining in social networks and improve existing data mining techniques. We propose an integrated data collection, management and analysis system. Furthermore, we propose specific analysis methods, such as topic classification, sentiment analysis, and seed selection algorithm to analyze social networks' data.
dc.description.tableofcontentsArchitecture and process of data collection and management system -- Topic related document classification -- Sentiment analysis using a probability model -- Dynamic seed selection -- Case study: data analysis in the view of mass communication
dc.formatapplication/pdf
dc.format.extentxiii, 104 pages
dc.genredissertations
dc.identifierdoi:10.13016/M2TD88
dc.identifier.otherDSP2015Lee
dc.identifier.urihttp://hdl.handle.net/11603/2088
dc.language.isoeng
dc.relation.ispartofTowson University Archives
dc.relation.ispartofTowson University Electronic Theses and Dissertations
dc.relation.ispartofTowson University Institutional Repository
dc.rightsCopyright protected, all rights reserved.
dc.titleText classification and sentiment analysis in social networks using a probability model
dc.typeText
dcterms.accessRightsThere are no restrictions on access to this document. An internet release form signed by the author to display this document online is on file with Towson University Special Collections and Archives.

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
DSP2015Lee_redacted.pdf
Size:
2.59 MB
Format:
Adobe Portable Document Format
Download

Collections

Towson University Graduate Theses and Dissertations