Automatic extraction of contextual valence shifters.

In opinion mining, many linguistic structures, called contextual valence shifters, may modify the prior polarity of items. Some systems of sentiment analysis have tried to take these shifters into account, but few studies have focused on the identification of all these structures and their impact on polarized words. In this paper, we describe a method that automatically identifies contextual valence shifters. It relies on a chi-square test applied to the contingency table representing the distribution of a candidate shifter in a corpus of reviews of diverse opinions. The approach depends on two resources - a corpus of reviews and a lexicon of valence terms - to build a list of contextual valence shifters. We also introduce a set of rules used to classify the extracted contextual valence shifters according to their impact on polarized words. They make use of the Pearson residuals in contingency tables to filter candidate shifters and classify them. We show that the technique reaches an F-measure of either 0.56 or 0.66, depending on how the categories of shifters are defined.

[1]  Stefan Trausan-Matu,et al.  The Impact of Valence Shifters on Mining Implicit Economic Opinions , 2010, AIMSA.

[2]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[3]  Janyce Wiebe,et al.  Effects of Adjective Orientation and Gradability on Sentence Subjectivity , 2000, COLING.

[4]  Annie Zaenen,et al.  Contextual Valence Shifters , 2006, Computing Attitude and Affect in Text.

[5]  Chu-Ren Huang,et al.  Sentiment Classification and Polarity Shifting , 2010, COLING.

[6]  J. Fleiss.  Measuring nominal scale agreement among many raters. , 1971 .

[7]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[8]  Ahmed Rafea,et al.  Improving Document-Level Sentiment Classification Using Contextual Valence Shifters , 2012, NLDB.

[9]  Janyce Wiebe,et al.  RECOGNIZING STRONG AND WEAK OPINION CLAUSES , 2006, Comput. Intell..

[10]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[11]  Alistair Kennedy,et al.  SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS , 2006, Comput. Intell..

[12]  Janyce Wiebe,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.

[13]  Jan Svartvik,et al.  A __ comprehensive grammar of the English language , 1988 .

[14]  Steven Abney,et al.  Semisupervised Learning for Computational Linguistics , 2007 .

[15]  Alan Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[16]  Syin Chan,et al.  Effectiveness of Simple Linguistic Processing in Automatic Sentiment Classification of Product Reviews , 2004 .

[17]  Noémi Boubel.  Extraction automatique de modifieurs de valence affective dans un texte. Etude exploratoire appliquée au cas de l'adverbe. , 2011 .

[18]  Noémi Boubel,et al.  Une procédure pour identifier les modifieurs de la valence affective d’un mot dans des textes (A procedure to identify modifiers of the word emotional valence in texts) , 2011, JEPTALNRECITAL.

[19]  Noémi Boubel.  Construction automatique d'un lexique de modifieurs de polarité (Automatic Construction of a Contextual Valence Shifters Lexicon) [in French] , 2012, JEP-TALN-RECITAL.

[20]  Isaac G. Councill,et al.  What's great and what's not: learning to classify the scope of negation for improved sentiment analysis , 2010, NeSp-NLP@ACL.