This system will be undergoing maintenance April 18th between 9:00AM and 12:00PM CDT.

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

Metadata describes a digital item, providing (if known) such information as creator, publisher, contents, size, relationship to other resources, and more. Metadata may also contain "preservation" components that help us to maintain the integrity of digital files over time.

Available Formats

Dublin Core: XML Dublin Core: JSON Dublin Core: Text Dublin Core: RDF/XML UNTL: XML Access METS: XML

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 acts
  • Keyword: binary classification models
  • 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.
Back to Top of Screen