Text Analysis for Product Reviews for Sentiment Analysis using NLP Methods

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-47 Number-8
Year of Publication : 2017
Authors : S.Muthukumaran, Dr.P.Suresh
DOI :  10.14445/22315381/IJETT-V47P278

Citation 

S.Muthukumaran, Dr.P.Suresh "Text Analysis for Product Reviews for Sentiment Analysis using NLP Methods", International Journal of Engineering Trends and Technology (IJETT), V47(8),474-480 May 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
This paper explains different methods for sentiment analysis and showcases an efficient methodology. It also highlights the importance the product reviews are of utmost importance for the buyers to decide based on their concerns regarding product`s various aspects for example a monitor, processor speed, memory etc. Hence this sentiment analysis of product review provides nearly accurate statistics regarding a product, providing an ease to the customers for analyzing the product and zero down his/her search for an online product. The key focus here is efficient feature extraction, polarity classification thereby summarizing positive and negative or neutral polarity. The proposed work is able to collect information from various sites and perform a sentiment analysis of a user reviews based on that information to rank a product. Also these reviews suffer from spammed reviews from unauthenticated users. In this paper, we can show that the statistical methods are often combined with traditional linguistic rules and representations. In view of these facts, we argue that the Naive Bayes classification model and Hidden Markov Models is applied to analyze the polarity of the sentiment on online product reviews due to its computational simplicity and stochastic robustness.

 References

[1]. Hemalatha, I., GP Saradhi Varma, and A. Govardhan. "Sentiment Analysis Tool using Machine Learning Algorithms."
[2]. Hemalatha, I., A. Govardhan, and G. P. Varma. "Machine Learning Methods in Classification of Text by Sentiment Analysis of Social Networks." International Journal of Advanced Research in Computer Science 2.5 (2011).
[3]. Hemalatha, I., GP Saradhi Varma, and A. Govardhan. "Preprocessing the Informal Text for efficient Sentiment Analysis." International Journal (2012).
[4]. G.P.Saradhi Varma,A.Govardhan, I.Hemalatha. "Sentiment Analysis Tool Using Machine Learning Algorithms." Elixir International Journal, Elixir Comp. Sci. & Engg. 58 (2013): 14791-14794.
[5].Y. Zou, C. Liu, Y. Jin, and B. Xie. Assessing Software Quality through Web Comment Search and Analysis. In Safe and Secure Software Reuse, pages 208–223. Springer, 2013.
[6]. N. Seyff, F. Graf, and N. Maiden. Using mobile re tools to give end-users their own voice. In Requirements Engineering Conference (RE), 2010 18th IEEE International, pages 37–46. IEEE, 2010.
[7]. C. Iacob and R. Harrison. Retrieving and analyzing mobile apps feature requests from online reviews. In MSR ?13 Proceedings of the 10th Working Conference on Mining Software Repositories, pages 41–44.IEEE Press, May 2013.
[8]. L. V. Galvis Carreño and K. Winbladh. Analysis of user comments: an approach for software requirements evolution. In ICSE ?13 Proceedings of the 2013 International Conference on Software Engineering, pages 582–591. IEEE Press, May 2013.
[9] D. Pagano and W. Maalej. User feedback in the appstore : an empirical study. In Proc. of the International Conference on Requirements Engineering - RE ?13, pages 125–134, 2013.
[10] K. Schneider, S. Meyer, M. Peters, F. Schliephacke, J. Mörschbach, and L. Aguirre. Product-Focused Software Process Improvement, volume 6156 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg, June 2010.
[11] K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak, and A. S. Peterson. Feature-oriented domain analysis (FODA) feasibility study. Technical report, DTIC Document, 1990.
[12] G. A. Miller. WordNet: a lexical database for English. Communications of the ACM, 38(11):39–41, 1995.
[13] A. Bagheri, M. Saraee and F. de Jong, `Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews`, Knowledge-Based Systems, vol. 52, pp. 20 1- 2 13,20 13.
[14] Chinsha, T. and S. Joseph. `A syntactic approach for aspect based opinion mining`, in the Proceeding of iEEE 9th International Conference on Semantic Computing (ICSC`15), pp. 24-3 1,20
[15] I. Pefialver-Martinez, F. Garcia-Sanchez, R. Valencia Garcia, M. Rodriguez-Garcia, V. Moreno, A. Fraga and J. Sanchez-Cervantes, `Feature-based opinion mining through ontologies`, Expert Systems with Applications, vol. 4 1, no. 13,pp. 5995-6008,20 14.
[16] Asghar, M.Z., et aI., `A Review of Feature Extraction in Sentiment Analysis`, Journal of Basic and Applied Scientific Research, vol. 4(3): pp. 18 1- 186,20 14.
[17] M. Eirinaki, S. Pisal and J. Singh, `Feature-based opinion mining and ranking`, Journal of Computer and System Sciences, vol. 78, no. 4, pp. 1 175- 1 184, 20 12.
[18] Isabella, J Analysis and evaluation of Feature selectors in opinion mining, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 3 No.6 Dec 2012-Jan 2013
[19] Qi Su,Kun Xiang,Houfeng Wang,Bin Sun and Shiwen Yu(2006).Using Pointwise Mutual Information to Identify Implicit Features in Customer Reviews.ICCPOL ,LNAI 4285 ,pp.22-30,Springer(2006).
[20] Edison Marrese-Taylor , Juan D. Vel asquez , Felipe Bravo- M arquez Yutaka Matsuo (2013). Identifying CustomerPreferences about Tourism Products using an Aspect-Based Opinion Mining Approach, Procedia Computer Science 22 (2013 ) 182 191,Elsevier
[21] Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification Using Machine Learning Techniques, In Proc. of EMNLP.
[22] Zhu J.,H.Wang,M,Zhu and B.K.Tsou.2011.Aspect based opinion polling from customer reviews. IEEE Transactions on Affective Computing,2(1):37-49.37
[23] Turney, p. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews, In Proceedings of the 40th Annual Meeting of the Association for Linguistics, Philadelphia, Pennsylvania.
[24] H. Wang, Y. Lu, and C. Zhai, “Latent aspect rating analysis on review text data: a rating regression approach,” in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010, pp. 783–792.
[25] Y. Lu, C. Zhai, and N. Sundaresan, “Rated aspect summarization of short comments,” in Proceedings of the 18th international conference on World Wide Web. ACM, 2009, pp. 131–140.
[26]. B. Snyder and R. Barzilay, “Multiple aspect ranking using the good grief algorithm,” in Proceedings of NAACL HLT, 2007, pp. 300–307.
[27]. S. Brody and N. Elhadad, “An unsupervised aspect-sentiment for online reviews,” in Proceedings of ACL: HLT, 2010, pp. 804–812.

Keywords
Sentiment analysis (SA), Opinion mining, Machine learning, Naive Bayes (NB), Support Vector Machine (SVM), online reviews, Hidden Markov (HMM).