Opinion Extraction Applied to Criteria

  • Benjamin Duthil
  • François Trousset
  • Gérard Dray
  • Jacky Montmain
  • Pascal Poncelet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7447)


The success of Information technologies and associated services (e.g., blogs, forums,...) eases the way to express massive opinion on various topics. Recently new techniques known as opinion mining have emerged. One of their main goals is to automatically extract a global trend from expressed opinions. While it is quite easy to get this overall assessment, a more detailed analysis will highlight that opinions are expressed on more specific topics: one will acclaim a movie for its soundtrack and another will criticize it for its scenario. Opinion mining approaches have little explored this multicriteria aspect. In this paper we propose an automatic extraction of text segments related to a set of criteria. The opinion expressed in each text segment is then automatically extracted. From a small set of opinion keywords, our approach automatically builds a training set of texts from the web. A lexicon reflecting the polarity of words is then extracted from this training corpus. This lexicon is then used to compute the polarity of extracted text segments. Experiments show the efficiency of our approach.


Recommender System Opinion Mining Human Expertise Sentiment Analysis Training Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Benjamin Duthil
    • 1
  • François Trousset
    • 1
  • Gérard Dray
    • 1
  • Jacky Montmain
    • 1
  • Pascal Poncelet
    • 2
  1. 1.EMA-LGI2PParc Scientifique Georges BesseNîmes CedexFrance
  2. 2.LIRMMUniversité Montpellier 2MontpellierFrance

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