Using appraisal groups for sentiment analysis

  • Authors:
  • Casey Whitelaw;Navendu Garg;Shlomo Argamon

  • Affiliations:
  • University of Sydney, Sydney, NSW, Australia;Illinois Institute of Technology, Chicago, IL;Illinois Institute of Technology, Chicago, IL

  • Venue:
  • Proceedings of the 14th ACM international conference on Information and knowledge management
  • Year:
  • 2005

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Abstract

Little work to date in sentiment analysis (classifying texts by `positive' or `negative' orientation) has attempted to use fine-grained semantic distinctions in features used for classification. We present a new method for sentiment classification based on extracting and analyzing appraisal groups such as ``very good'' or ``not terribly funny''. An appraisal group is represented as a set of attribute values in several task-independent semantic taxonomies, based on Appraisal Theory. Semi-automated methods were used to build a lexicon of appraising adjectives and their modifiers. We classify movie reviews using features based upon these taxonomies combined with standard ``bag-of-words'' features, and report state-of-the-art accuracy of 90.2%. In addition, we find that some types of appraisal appear to be more significant for sentiment classification than others.