Entity Centric Opinion Mining from Blogs

With the growth of web 2.0, people are using it as a medium to express their opinion and thoughts. With the explosion of blogs, journal like user-generated content on the web, companies, celebrities and politicians are concerned about mining and analyzing the discussions about them or their products. In this paper, we present a method to perform opinion mining and summarize opinions at entity level for English blogs. We first identify various objects (named entities) which are talked about by the blogger, then we identify the modifiers which modify the orientation towards these objects. Finally, we generate object centric opinionated summary from blogs. We perform experiments like named entity identification, entity-modifier relationship extraction and modifier orientation estimation. Experiments and Results presented in this paper are cross verified with the judgment of human annotators.

[1]  Wei Zhang,et al.  Opinion retrieval from blogs , 2007, CIKM '07.

[2]  Craig MacDonald,et al.  An effective statistical approach to blog post opinion retrieval , 2008, CIKM '08.

[3]  Peter D. Turney.  Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL , 2001, ECML.

[4]  Bing Liu,et al.  Mining Opinion Features in Customer Reviews , 2004, AAAI.

[5]  Cynthia Whissell,et al.  THE DICTIONARY OF AFFECT IN LANGUAGE , 1989 .

[6]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[7]  Tejashri Inadarchand Jain,et al.  Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2010 .

[8]  Barbara Plank,et al.  Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10) , 2010 .

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

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

[11]  Prem Melville,et al.  Sentiment analysis of blogs by combining lexical knowledge with text classification , 2009, KDD.

[12]  Fadi Biadsy,et al.  Contextual Phrase-Level Polarity Analysis Using Lexical Affect Scoring and Syntactic N-Grams , 2009, EACL.

[13]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[14]  Wai Lam,et al.  MEAD - A Platform for Multidocument Multilingual Text Summarization , 2004, LREC.

[15]  Lipika Dey,et al.  Opinion mining from noisy text data , 2008, AND '08.

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

[17]  Gérard Dray,et al.  Opinion Mining From Blogs , 2009, CISIM 2009.

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

[19]  Man Lung Yiu,et al.  Group-by skyline query processing in relational engines , 2009, CIKM.

[20]  Balaraman Ravindran,et al.  Latent dirichlet allocation based multi-document summarization , 2008, AND '08.

[21]  Hsin-Hsi Chen,et al.  Opinion Extraction, Summarization and Tracking in News and Blog Corpora , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[22]  Rohini K. Srihari,et al.  Using Verbs and Adjectives to Automatically Classify Blog Sentiment , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.