Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads

Description:

Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.

Creator(s):
Creation Date: October 2012
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Total Uses: 87
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Creator (Author):
Biyani, Prakhar

Pennsylvania State University

Creator (Author):
Bhatia, Sumit

Pennsylvania State University

Creator (Author):
Caragea, Cornelia

University of North Texas

Creator (Author):
Mitra, Prasenjit

Pennsylvania State University

Publisher Info:
Place of Publication: [New York, New York]
Date(s):
  • Creation: October 2012
Description:

Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.

Degree:
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

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.

Physical Description:

16 p.

Language(s):
Subject(s):
Keyword(s): dialogue act | binary classification | online forums | subjectivity
Source: Proceedings of the Twenty-Fourth International Conference on Computational Linguistics (COLING), 2012, Mumbai, India
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • DOI: 10.1145/2396761.2398675 |
  • ARK: ark:/67531/metadc181701
Resource Type: Paper
Format: Text
Rights:
Access: Public