Textual affect sensing and affective communication

Mitsuru Ishizuka, Alena Neviarouskaya, Mostafa Al Masum Shaikh

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Unlike sentiment analysis which detects positive, negative, or neutral sentences, textual affect sensing tries to detect more detailed affective or emotional states appearing in text, such as joy, sadness, anger, fear, disgust, surprise and much more. The authors describe here their following two approaches for textual affect sensing: The first one detects nine emotions using a set of rules implemented on the basis of a linguistic compositionality principle for textual affect interpretation. This process includes symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. The second one challenged to recognize 22 emotion types defined in the OCC (Ortony, Clore & Collins) emotion model, which is the most comprehensive emotion model and employs several cognitive variables. In this research, we have shown how these cognitive variables of the emotion model can be computed from linguistic components in text. These two approaches have exploited detailed level analyses of text in two different ways more than ever towards textual affect sensing. Applications towards affective communication are also outlined, including affective instant messaging, affective chat in 3D virtual world, affective haptic interaction, and online news classification relying on affect.

Original languageEnglish
Pages (from-to)81-102
Number of pages22
JournalInternational Journal of Cognitive Informatics and Natural Intelligence
Volume6
Issue number4
DOIs
Publication statusPublished - 2012 Oct
Externally publishedYes

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Keywords

  • Affective communication
  • Emotions
  • Instant messaging
  • Linguistic compositionality
  • Textual affect sensing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

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