Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification

Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively, improving the baseline using just the average word polarities by about 2% points.

[1]  Philip S. Yu,et al.  A holistic lexicon-based approach to opinion mining , 2008, WSDM '08.

[2]  Hung-Yu Kao,et al.  A Categorized Sentiment Analysis of Chinese Reviews by Mining Dependency in Product Features and Opinions from Blogs , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[3]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[4]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

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

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

[7]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[8]  Yue Lu,et al.  Latent aspect rating analysis on review text data: a rating regression approach , 2010, KDD.

[9]  Markus Zanker,et al.  Classification of Customer Reviews based on Sentiment Analysis , 2012, ENTER.

[10]  Bruno Ohana,et al.  Sentiment Classification of Reviews Using SentiWordNet , 2009 .

[11]  Masaru Kitsuregawa,et al.  Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents , 2007, EMNLP.

[12]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[13]  Mitsuru Ishizuka,et al.  SentiFul: Generating a reliable lexicon for sentiment analysis , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[14]  Timothy W. Finin,et al.  Delta TFIDF: An Improved Feature Space for Sentiment Analysis , 2009, ICWSM.