Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads Metadata
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- Main Title Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads
Author: Biyani, PrakharCreator Type: PersonalCreator Info: Pennsylvania State University
Author: Bhatia, SumitCreator Type: PersonalCreator Info: Pennsylvania State University
Author: Caragea, CorneliaCreator Type: PersonalCreator Info: University of North Texas
Author: Mitra, PrasenjitCreator Type: PersonalCreator Info: Pennsylvania State University
Organizer of meeting: Association for Computing MachineryContributor Type: Organization
Name: Association for Computing MachineryPlace of Publication: [New York, New York]
- Creation: 2012-10
- Physical Description: 16 p.
- Content Description: Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.
- Keyword: dialogue act
- Keyword: binary classification
- Keyword: online forums
- Keyword: subjectivity
- Conference: Proceedings of the Twenty-Fourth International Conference on Computational Linguistics (COLING), 2012, Mumbai, India
Name: UNT Scholarly WorksCode: UNTSW
Name: UNT College of EngineeringCode: UNTCOE
- Rights Access: public
- DOI: 10.1145/2396761.2398675
- Archival Resource Key: ark:/67531/metadc181701
- Academic Department: Computer Science and Engineering
- Display Note: This is the authors manuscript version of the paper. The final definitive version can be found through the Association of Computing and Machinery (ACM): http://dl.acm.org/citation.cfm?doid=2396761.2398675
- Display Note: Abstract: Subjectivity analysis has been actively used in various applications such as opinion mining of customer reviews in online review sites, questions-answering in CQA sites, multi-document summarization, etc. However, there has been very little focus on subjectivity analysis in the domain of online forums. Online forums contain huge amounts of user-generated data in the form of discussions between forum members on specific topics and are a valuable source of information. In this work, we perform subjectivity analysis of online forum threads. We model the task as a binary classification of threads in one of the two classes: subjective and non-subjective. Unlike previous works on subjectivity analysis, we use several non-lexical thread-specific features for identifying subjectivity orientation of threads. We evaluate our methods by comparing them with several state-of-the art subjectivity analysis techniques. Experimental results on two popular online forums demonstrate that our methods outperform strong baselines in most of the cases.