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Sentiment Extraction and Analysis of Product Reviews at Sentence Level

Rajashree Shettar

Abstract


These days large number of people are using internet as a medium to communicate and express their views on politics, products, movies and other various domains. Monitoring these views online will be helpful for the companies, individuals or groups, who are interested in knowing what the customers are talking about their products. But manually monitoring is a costly and time consuming task. Using sentiment analysis one can perform automatic summarization of the customer views based on their opinions. This paper presents work carried out to perform sentiment extraction and analysis of the product reviews at sentence level. Machine learning techniques, namely Multinomial Naïve Bayes (MNB) and Support Vector Machines (SVM)) were used to perform sentiment classification. Experimentation was carried out with two different data corpus and results of the two machine learning techniques were compared. Cross-domain sentiment analysis was also studied and its result is presented. The sentiment analysis system presented here also identifies the product features and extracts the sentiments associated with it. Finally, challenges face in sentiment analysis and future works are discussed.

Keywords


Machine Learning Opinion Mining, Sentiment Analysis, Text Classification.

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References


B Liu, "Sentiment Analysis and Subjectivity", Handbook of Natural language Processing, Second Edition, 2010, ISBN 9781420085921, pages 627-666.

Jeff Zabin and Alex Jefferies, “Social Media Monitoring and Analysis: Generating Consumer Insights from Online Conversation.” Aberdeen Group Benchmark Report, January 2008, pages 1-28.

Fabrizio Sebastiani, "Machine Learning in Automated Text Categorization", Journal, ACM Computing Surveys (CSUR), Volume 34 Issue 1, March 2002, Digital ObjectIdentifier: 10.1145/505282.505283, pages: 1 - 47.

Andrew McCallum, Kamal Nigam, "A Comparison of Event Models for Naive Bayes Text Classification.” In AAAI/ICML-98 Workshop on Learning for Text Categorization, Wisconsin, 1998, pages 41-48.

Thorsten Joachims, "Text Categorization with Support Vector Machines: Learning with Many Relevant Features.” In Proceedings of 10th European Conference on Machine Learning, Germany 1998, pages 137-142.

Marie-Catherine de Marneffe and Christopher D. Manning, "Stanford Typed Dependencies Manual", Stanford University, Technical Report, September 2008.

B. Pang, L. Lee, S. Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques”, Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, 2002, Digital Object Identifier 10.3115/1118693.1118704, Volume 10, pages 79-86.

B.Pang, Lillian Lee, "A Sentimental Education: Sentiment Analysis using Subjectivity Summarization based on Minimum Cuts", Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Stroudsburg, 2004, Digital Object Identifier 10.3115/1218955.1218990, Article No. 271.

Alec Go, Lei Huang, and Richa Bhayani, “Twitter Sentiment Analysis”, Final Projects from CS224N for Spring 2008/2009 at The Stanford Natural Language Processing Group.

Tetsuya Nasukawa, Jeonghee Yi, "Sentiment Analysis: Capturing Favorability using Natural Language Processing", Proceedings of the 2nd International Conference on Knowledge Capture, New York, 2003, Digital Object Identifier 10.1145/945645.945658, pages 70-77.

Bo Pang, Lillian Lee, "Opinion Mining and Sentiment Analysis", Foundations and Trends in Information Retrieval, 2008,DigitalObjectIdentifier 10.1561/1500000011, Volume 2, Issue 1-2, pages 1-135.

X.Ding, B.Liu, P.S. Yu, "A Holistic Lexicon-Based Approach to Opinion Mining." Proceedings of the International Conference on Web Search and Web Data Mining, New York, 2008, Digital Object Identifier 10.1145/1341531.1341561, pages 231-240.

Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, Manfred Stede, "Lexicon-Based Methods for Sentiment Analysis", Association for Computational Linguistics, 2011, Digital Object Identifier 10.1162/COLI_a_00049, Volume 37, No. 2, pages 267-307.

Bruno Ohana, Brendan Tierney, "Opinion Mining with SentiWordNet", Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains, 2011, Digital Object Identifier 10.4018/978-1-60960-067-9.ch013, pages 266-286.

Robert Blumberg and Shaku Atre, "The Problem with Unstructured Data", DM Review Magazine, Volume: 13, Issue: Feb. 2003, ISSN: 15212912, pages 42-49.

B. Pang and L. Lee, "Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales", Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Stroudsburg, 2005, Digital Object Identifier 10.3115/1219840.1219855, pages 115-124.

Marie-Catherine de Marneffe, Bill MacCartney and Christopher D. Manning, “Generating Typed Dependency Parses from Phrase Structure Parses”, In Proceedings of LREC-06, Italy, 2006, pages 449-454.

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten, "The WEKA Data Mining Software: An Update." SIGKDD Explorations Newsletter, New York, 2009, Digital Object Identifier 10.1145/1656274.1656278, Volume 11, Issue 1, pages 10-18.

Cecilia Ovesdotter Alm, “Association for Computational Linguistics”, Proceedings of the 49th Annual Meeting of the pages 107–112, Portland, Oregon, June 19-24, 2011.

K Cai, S Spangler, Y Chen, “Web Intelligence and Intelligent Agent Technology”, IEEEWICACM International Conference on WICACM (2008) Volume:1,Pages:265-271DOI: 10.1109/WIIAT.2008.188


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