Review sentiment scoring via a parse-and-paraphrase paradigm

  • Authors:

  • Jingjing Liu;Stephanie Seneff

  • Affiliations:

  • MIT Computer Science & Artificial Intelligence Laboratory, Cambridge, MA;MIT Computer Science & Artificial Intelligence Laboratory, Cambridge, MA

  • Venue:
  • EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
  • Year:
  • 2009

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Abstract

This paper presents a parse-and-paraphrase paradigm to assess the degrees of sentiment for product reviews. Sentiment identification has been well studied; however, most previous work provides binary polarities only (positive and negative), and the polarity of sentiment is simply reversed when a negation is detected. The extraction of lexical features such as unigram/bigram also complicates the sentiment classification task, as linguistic structure such as implicit long-distance dependency is often disregarded. In this paper, we propose an approach to extracting adverb-adjective-noun phrases based on clause structure obtained by parsing sentences into a hierarchical representation. We also propose a robust general solution for modeling the contribution of adverbials and negation to the score for degree of sentiment. In an application involving extracting aspect-based pros and cons from restaurant reviews, we obtained a 45% relative improvement in recall through the use of parsing methods, while also improving precision.