Vol: 10, Issue: 1 2020

Page: 38-42

Developing an Effective Sentiment Analysis for Reviewing a Product

Aarushi Chawla

Received Date: 2020-01-27

Accepted Date: 2020-02-06

Published Date: 2020-03-02

Analysis of sentiment and assessment mining is the area of study that researches individuals' viewpoints, opinions, mentalities, and feelings from composed language. Sentiment examination frameworks are being applied in pretty much every business and social area, which assists them with dissecting consumer loyalty of their item since conclusions are integral to practically all human exercises and are key impacts of our practices. The proposed structure for Movie Review comprises information collection, pre-handling, and estimation of consumer loyalty. Information mining is used in the Data collection and pre-processing stage to arrange client audit-based word references of properties and opinion words. Then, at that point, utilizing opinion analysis, feeling scores for subtleties are determined for every Movie Review. We will direct an observational contextual investigation on client surveys of films. We accept that our proposed client report-based methodology saves time and effort in estimating consumer loyalty and takes the genuine opinions of clients.

References

• Bing Liu,"Exploring User Opinions in Recommender Systems", Proceeding of the second KDD workshop on Large Scale Recommender Systems and the Netflix Prize Competition", Aug 24, 2008, Las Vegas, Nevada, USA.
• Xiaowen Ding. A Holistic Lexicon-Based Approach to Opinion Mining- WSDM’08, February 11- 12, 2008, Palo Alto, California, USA.2008 ACM 978-1-59593-927- 9/08/0002.
• R. B. W. N. Jeonghee Yi, T Nasukawa, “Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques,” ICDM03, IEEE, 2003
• D. A. A. S.M.Shamimul Hasan, “Proximity-based sentiment analysis,” IEEE, 2011
• . S. Mohammad, B. Dorr, and C. Dunne, “ Generating HighCoverage Semantic Orientation Lexicons fom Overly Marked Words and a Thesaurus”, In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing,pp. 599-608, 2009.
• D. Rao and D. Ravichandan, “ Semi-supervised polarity lexicon induction”, In Proceeding of 12 conference of the European Chapter of the Association for Computational Linguistics, pp. 675-682, USA, 2009.
• P.J. Stone, “The General Inquirer: A Computer Approach to Content Analysis”, The MIT Press, 1966.
• K. Dave, S. Lawrence and D. M. Pennock, “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews”, In Proceedings of 12th International Conference on World Wide Web, pp. 519- 528, Hungary, 2003.
• S. M. Kim and E. Hovy, “Determining the sentiment of opinions”, In Proceeding of COLING, pp. 1367-1373, 2004.
• A. Das and S. Bandyopadhay, “SentiWordNet for Indian languages,” Asian Federation for Natural Language Processing, China, pp. 56–63, August 2010.
• Michael Gamon, Anthony Aue, Simon Corston-Oliver, and Eric Ringger, “Pulse: Mining Customer Opinions from Free Text Natural Language Processing”, Microsoft Research, Redmond, WA 98052, USA.
• Minqing Hu and Bing Liu, “Mining and Summarizing Customer Reviews”, Proceedings of the tenth ACM SIGKDD International conference on knowledge discovery in data mining (KDD-2004), August 22-25.
• Guang Qiu, Bing Liu, Jiajun Bu and Chun Chen. "Opinion Word Expansion and Target Extraction through Double Propagation." Computational Linguistics, March 2011, Vol. 37, No. 1: 9.27.
• Lie Zhang and Bing Liu. "Identifying Noun Product Features that Imply Opinions." ACL-2011 (short paper), Portland, Oregon, USA, June 19-24, 2011.
• Xiaowen Ding. A Holistic Lexicon-Based Approach to Opinion Mining- WSDM’08, February 11- 12, 2008, Palo Alto, California, USA.2008 ACM 978-1-59593-927- 9/08/0002.