Generating contextualized sentiment lexica based on latent topics and user ratings

  • Authors:
  • Ralf Krestel;Stefan Siersdorfer

  • Affiliations:
  • University of California, Irvine, Irvine;L3S Research Center, Hannover, Germany

  • Venue:
  • Proceedings of the 24th ACM Conference on Hypertext and Social Media
  • Year:
  • 2013

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Abstract

Sentiment lexica are useful for analyzing opinions in Web collections, for domain-dependent sentiment classification, and as sub-components of recommender systems. In this paper, we present a strategy for automatically generating topic-dependent lexica from large corpora of review articles by exploiting accompanying user ratings. Our approach combines text segmentation, discriminative feature analysis techniques, and latent topic extraction to infer the polarity of n-grams in a topical context. Our experiments on rating prediction demonstrate a substantial performance improvement in comparison with existing state-of-the-art sentiment lexica.