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A Comprehensive Review of Aspect Identification and Sentiment Classification Techniques for Users Reviews
Abstract
Nowadays Merchants sell their products to the customers using online websites and also ask their customers to give their reviews about the purchased product. As e-commerce is becoming more popular, the reviews for the product are also increasing more rapidly. If a product is popular and good, there will be hundreds of reviews for that particular product. So it makes difficulty for the customers to read all the opinions in the reviews and abstract the most important opinions. It is also difficult for the manufacturer to manage and view all the customer reviews. The reviews which are given by the users are unorganized. And also it is inefficient for the customers to read all the opinions of the users to identify the important aspects. A single product may have several aspects. Some aspects for a particular product play a major role in deciding about the whole product, and they also depict the whole product. Identification of the important aspects becomes as an essential one, so customers can concentrate on that aspect and easily buy that product without any confusion. The aspects which play an important role in the reviews should be abstracted and then it should be classified to identify whether it is a positive or negative opinion by the customer. This paper explains about the various techniques and their description, to identify the important aspects for a product and to classify them.
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References
Rutuja Tikait, Ranjana Badre, Mayura Kinikar, Product Aspect Ranking Techniques: A Survey,International Journal of Innovative Research in Computer and Communication Engineering Vol. 2, Issue 11,Nov 2014.
M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proc. SIGKDD, Seattle, WA, USA, Vol 3, 2004, pp. 168–177.
Ohana and B. Tierney, “Sentiment classification of reviews using SentiWordNet,” in Proc. IT&T Conf., Dublin, Ireland,Dec 2009.
Zheng-Jun Zha, Jianxing Yu, Jinhui Tang, Meng Wang, and Tat-Seng Chua,” Product Aspect Ranking and Its Applications”, IEEE transactions on knowledge ans data engineering, Vol. 26, No.5, May 2014.
B. Liu, M. Hu, and J. Cheng, “Opinion observer: Analyzing and comparing opinions on the web,” in Proc. 14th Int. Conf. WWW, Chiba, Japan, 2005, pp. 342–351.
B. Liu, “Sentiment analysis and subjectivity,” in Handbook of Natural Language Processing, New York, NY, USA: Marcel Dekker, Inc., Jan 2009.
B. Pang and L. Lee, “Opinion mining and sentiment analysis,” in Found. Trends Inform. Retrieval, vol. 2, no. 1–2, pp. 1–135, 2008.
Hu Minging, Liu Bing. Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD’04); Oct 2004.
Kim S, Hovy E. Determining the sentiment of opinions. In: Proceedings of interntional conference on Computational Linguistics (COLING’04); Jun 2004.
Mohammad S, Dunne C, Dorr B. Generating high-coverage semantic orientation lexicons from overly marked words and a thesaurus. In:Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP’09); July 2009.
Kang Hanhoon, Yoo Seong Joon, Han Dongil. Senti-lexicon and improved Naı¨ve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst Appl, Mar 2012.
A. M. Popescu and O. Etzioni, “Extracting product features and opinions from reviews,” in Proc. HLT/EMNLP, Vancouver, BC, Canada, 2005, pp. 339–346.
X. Ding, B. Liu, and P. S. Yu, “A holistic lexicon- based approach to opinion mining,” in Proc. WSDM, New York, NY, USA, 2008, pp. 231–240.
Kaufmann JM. JMaxAlign: A Maximum Entropy Parallel Sentence Alignment Tool. In: Proceedings of COLING’12: Demonstration Papers, Mumbai; p. 277–88, Oct 2012.
Y. Wu, Q. Zhang, X. Huang, and L. Wu, “Phrase dependency parsing for opinion mining,” in Proc. ACL, Singapore, 2009, pp. 1533–1541.
F. Li et al., “Structure-aware review mining and summarization,”in Proc. 23rd Int. Conf. COLING, Beijing, China, Jan 2010, pp. 653–661
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