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

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Title

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

Creator

  • Author: Biyani, Prakhar
    Creator Type: Personal
    Creator Info: Pennsylvania State University
  • Author: Bhatia, Sumit
    Creator Type: Personal
    Creator Info: Pennsylvania State University
  • Author: Caragea, Cornelia
    Creator Type: Personal
    Creator Info: University of North Texas
  • Author: Mitra, Prasenjit
    Creator Type: Personal
    Creator Info: Pennsylvania State University

Contributor

  • Organizer of meeting: Association for Computing Machinery
    Contributor Type: Organization

Publisher

  • Name: Association for Computing Machinery
    Place of Publication: [New York, New York]

Date

  • Creation: 2012-10

Language

  • English

Description

  • Physical Description: 16 p.
  • Content Description: Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.

Subject

  • Keyword: dialogue act
  • Keyword: binary classification
  • Keyword: online forums
  • Keyword: subjectivity

Source

  • Conference: Proceedings of the Twenty-Fourth International Conference on Computational Linguistics (COLING), 2012, Mumbai, India

Collection

  • Name: UNT Scholarly Works
    Code: UNTSW

Institution

  • Name: UNT College of Engineering
    Code: UNTCOE

Rights

  • Rights Access: public

Resource Type

  • Paper

Format

  • Text

Identifier

  • DOI: 10.1145/2396761.2398675
  • Archival Resource Key: ark:/67531/metadc181701

Degree

  • Academic Department: Computer Science and Engineering

Note

  • 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.