Affect analysis of web forums and blogs using correlation ensembles

Ahmed Abbasi, Hsinchun Chen, Sven Thoms, Tianjun Fu

Research output: Contribution to journalArticle

97 Scopus citations

Abstract

Analysis of affective intensities in computer-mediated communication is important in order to allow a better understanding of online users' emotions and preferences. Despite considerable research on textual affect classification, it is unclear which features and techniques are most effective. In this study, we compared several feature representations for affect analysis, including learned n-grams and various automatically and manually crafted affect lexicons. We also proposed the support vector regression correlation ensemble (SVRCE) method for enhanced classification of affect intensities. SVRCE uses an ensemble of classifiers each trained using a feature subset tailored toward classifying a single affect class. The ensemble is combined with affect correlation information to enable better prediction of emotive intensities. Experiments were conducted on four test beds encompassing web forums, blogs, and online stories. The results revealed that learned n-grams were more effective than lexicon-based affect representations. The findings also indicated that SVRCE outperformed comparison techniques, including Pace regression, semantic orientation, and WordNet models. Ablation testing showed that the improved performance of SVRCE was attributable to its use of feature ensembles as well as affect correlation information. A brief case study was conducted to illustrate the utility of the features and techniques for affect analysis of large archives of online discourse.

Original languageEnglish (US)
Article number4479460
Pages (from-to)1168-1180
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume20
Issue number9
DOIs
StatePublished - Sep 1 2008

Keywords

  • Affective computing
  • Discourse
  • Emotion recognition
  • Linguistic processing
  • Machine learning
  • Text mining

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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