Automatically determining attitude type and force for sentiment analysis

Shlomo Argamon, Kenneth Bloom, Andrea Esuli, Fabrizio Sebastiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

Recent work in sentiment analysis has begun to apply fine-grained semantic distinctions between expressions of attitude as features for textual analysis. Such methods, however, require the construction of large and complex lexicons, giving values for multiple sentiment-related attributes to many different lexical items. For example, a key attribute is what type of attitude is expressed by a lexical item; e.g., beautiful expresses appreciation of an object's quality, while evil expresses a negative judgment of social behavior. In this chapter we describe a method for the automatic determination of complex sentiment-related attributes such as attitude type and force, by applying supervised learning to WordNet glosses. Experimental results show that the method achieves good effectiveness, and is therefore well-suited to contexts in which these lexicons need to be generated from scratch.

Original languageEnglish
Title of host publicationHuman Language Technology
Subtitle of host publicationChallenges of the Information Society - Third Language and Technology Conference, LTC 2007, Revised Selected Papers
Pages218-231
Number of pages14
DOIs
Publication statusPublished - 28 Sep 2009
Event3rd Language and Technology Conference, LTC 2007 - Poznan, Poland
Duration: 5 Oct 20077 Oct 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5603 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Language and Technology Conference, LTC 2007
CountryPoland
CityPoznan
Period5/10/077/10/07

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Keywords

  • Appraisal theory
  • Lexicon learning
  • Sentiment analysis
  • WordNet

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Argamon, S., Bloom, K., Esuli, A., & Sebastiani, F. (2009). Automatically determining attitude type and force for sentiment analysis. In Human Language Technology: Challenges of the Information Society - Third Language and Technology Conference, LTC 2007, Revised Selected Papers (pp. 218-231). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5603 LNAI). https://doi.org/10.1007/978-3-642-04235-5_19