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

16 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages218-231
Number of pages14
Volume5603 LNAI
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event3rd Language and Technology Conference, LTC 2007 - Poznan
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)03029743
ISSN (Electronic)16113349

Other

Other3rd Language and Technology Conference, LTC 2007
CityPoznan
Period5/10/077/10/07

Fingerprint

Sentiment Analysis
Supervised learning
Semantics
Attribute
Express
Social Behavior
WordNet
Supervised Learning
Experimental Results

Keywords

  • Appraisal theory
  • Lexicon learning
  • Sentiment analysis
  • WordNet

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Argamon, S., Bloom, K., Esuli, A., & Sebastiani, F. (2009). Automatically determining attitude type and force for sentiment analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5603 LNAI, 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

Automatically determining attitude type and force for sentiment analysis. / Argamon, Shlomo; Bloom, Kenneth; Esuli, Andrea; Sebastiani, Fabrizio.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5603 LNAI 2009. p. 218-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5603 LNAI).

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

Argamon, S, Bloom, K, Esuli, A & Sebastiani, F 2009, Automatically determining attitude type and force for sentiment analysis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5603 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5603 LNAI, pp. 218-231, 3rd Language and Technology Conference, LTC 2007, Poznan, 5/10/07. https://doi.org/10.1007/978-3-642-04235-5_19
Argamon S, Bloom K, Esuli A, Sebastiani F. Automatically determining attitude type and force for sentiment analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5603 LNAI. 2009. p. 218-231. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-04235-5_19
Argamon, Shlomo ; Bloom, Kenneth ; Esuli, Andrea ; Sebastiani, Fabrizio. / Automatically determining attitude type and force for sentiment analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5603 LNAI 2009. pp. 218-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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