IJE TRANSACTIONS B: Applications Vol. 30, No. 11 (November 2017) 1428-1437    Article in Press

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S. KOTAGIRI, M. B. Kalva and M. A
( Received: May 26, 2017 – Accepted: September 08, 2017 )

Abstract    As the internet and its applications are growing, E-commerce has become one of its rapid applications. Customers of E-commerce were provided with theopportunity to express their opinion about theproduct on the web as a text in the form of reviews. In the previous studies, mere founding sentiment from reviews was not helpful to get theexact opinion of the review. In this paper, we have used Aspect-Based Opinion Mining to get more Interesting aspects of a product’ssentiment from unlabelled textual data. First noun phrases algorithm was used to get all the aspect term of a review sentence. Secondly get sentiment algorithm was applied on the result of thenoun-phrase algorithm. Finally using relativeimportance algorithm important aspects were presented to the user. Our proposed methodology has achieved 77.03% of accuracy compared to previews studies. The proposed methodology can be applied for any product reviews in the form of text without any label, and it does not require any training dataset.


Keywords    Sentiment analysis, Opinion mining, Aspect term, Aspect based analysis, Customer review


References    Turney, Peter (2002). \\\\\\\\\\\\\\\"Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews\\\\\\\\\\\\\\\". Proceedings of the Association for Computational Linguistics. pp. 417–424. arXiv:cs.LG/0212032.Pang, Bo; Lee, Lillian; Vaithyanathan, Shivakumar (2002). \\\\\\\\\\\\\\\"Thumbs up? Sentiment Classification using Machine Learning Techniques\\\\\\\\\\\\\\\". Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 79–86.I. Pang, Bo; Lee, Lillian (2005). \\\\\\\\\\\\\\\"Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales\\\\\\\\\\\\\\\". Proceedings of the Association for Computational Linguistics (ACL). pp. 115–124. Snyder, Benjamin; Barzilay, Regina (2007). \\\\\\\\\\\\\\\"Multiple Aspect Ranking using the Good Grief Algorithm\\\\\\\\\\\\\\\". Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL). pp. 300–307. Vryniotis, Vasilis (2013). The importance of Neutral Class in Sentiment Analysis Koppel, Moshe; Schler, Jonathan (2006). \\\\\\\\\\\\\\\"The Importance of Neutral Examples for Learning Sentiment\\\\\\\\\\\\\\\". Computational Intelligence 22. pp. 100–109. CiteSeerX, Filipe Nunes; Araujo, Matheus (2010). \\\\\\\\\\\\\\\"A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods\\\\\\\\\\\\\\\". Transactions on Embedded Computing Systems. 9 (4).Hu, Minqing; Liu, Bing (2004). \\\\\\\\\\\\\\\"Mining and Summarizing Customer Reviews\\\\\\\\\\\\\\\". Proceedings of KDD 2004.Cataldi, Mario; Ballatore, Andrea; Tiddi, Ilaria; Aufaure, Marie-Aude (2013-06-22). \\\\\\\\\\\\\\\"Good location, terrible food: detecting feature sentiment in user-generated reviews\\\\\\\\\\\\\\\". Social Network Analysis and Mining. 3 (4): 1149–1163. doi:10.1007/s13278-013-0119-7. ISSN 1869-5450Liu, Bing; Hu, Minqing; Cheng, Junsheng (2005). \\\\\\\\\\\\\\\"Opinion Observer: Analyzing and Comparing Opinions on the Web\\\\\\\\\\\\\\\". Proceedings of WWW 2005.Zhai, Zhongwu; Liu, Bing; Xu, Hua; Jia, Peifa (2011-01-01). Huang, Joshua Zhexue; Cao, Longbing; Srivastava, Jaideep, eds. Constrained LDA for Grouping Product Features in Opinion Mining. Lecture Notes in Computer Science. Springer BerlinHeidelberg.pp. 448–459. doi:10.1007/978-3-642-20841-6_37. ISBN 978-3-642-20840-9.Titov, Ivan; McDonald, Ryan (2008-01-01). \\\\\\\\\\\\\\\"Modeling Online Reviews with Multi-grain Topic Models\\\\\\\\\\\\\\\". Proceedings of the 17th International Conference on World Wide WebLiu, Bing (2010). \\\\\\\\\\\\\\\"Sentiment Analysis and Subjectivity\\\\\\\\\\\\\\\" (PDF). In Indurkhya, N Damerau, F. J. Handbook of Natural Language Processing (Second ed.).  

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