Using non-lexical features for identifying factual and opinionative threads in online forums

Prakhar Biyani, Sumit Bhatia, Cornelia Caragea, Prasenjit Mitra

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Subjectivity analysis essentially deals with separating factual information and opinionative information. It has been actively used in various applications such as opinion mining of customer reviews in online review sites, improving answering of opinion questions in community question-answering (CQA) sites, multi-document summarization, etc. However, there has not been much 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 (seeking opinions, emotions, other private states) and non-subjective (seeking factual information). 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.

Original languageEnglish
Pages (from-to)170-178
Number of pages9
JournalKnowledge-Based Systems
Volume69
Issue number1
DOIs
Publication statusPublished - 2014
Externally publishedYes

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Keywords

  • Classification
  • Dialog act
  • Online forums
  • Subjectivity analysis
  • Supervised learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Management Information Systems
  • Information Systems and Management

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