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Reseach Article

Sentiment Analysis of Customer Review Data using Big Data: A Survey

Published on March 2017 by Mugdha Jinturkar, Pradnya Gotmare
Emerging Trends in Computing
Foundation of Computer Science USA
ETC2016 - Number 1
March 2017
Authors: Mugdha Jinturkar, Pradnya Gotmare

Mugdha Jinturkar, Pradnya Gotmare . Sentiment Analysis of Customer Review Data using Big Data: A Survey. Emerging Trends in Computing. ETC2016, 1 (March 2017), 3-8.

author = { Mugdha Jinturkar, Pradnya Gotmare },
title = { Sentiment Analysis of Customer Review Data using Big Data: A Survey },
journal = { Emerging Trends in Computing },
issue_date = { March 2017 },
volume = { ETC2016 },
number = { 1 },
month = { March },
year = { 2017 },
issn = 0975-8887,
pages = { 3-8 },
numpages = 6,
url = { /proceedings/etc2016/number1/27300-6251/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 Emerging Trends in Computing
%A Mugdha Jinturkar
%A Pradnya Gotmare
%T Sentiment Analysis of Customer Review Data using Big Data: A Survey
%J Emerging Trends in Computing
%@ 0975-8887
%V ETC2016
%N 1
%P 3-8
%D 2017
%I International Journal of Computer Applications

Rapid evolution in technology and the internet brought us to the era of online services. E-commerce is nothing but trading goods or services online. Many customers share their good or bad opinions about products or services online nowadays. These opinions become a part of the decision-making process of consumer and make an impact on the business model of the provider. Also, understanding and considering reviews will help to gain the trust of the customer which will help to expand the business. Many users give reviews for the single product. Such thousands of review can be analyzed using big data effectively. The results can be presented in a convenient visual form for the non-technical user. Thus, the primary goal of research work is the classification of customer reviews given for the product in the map-reduce framework.

  1. Wang, F. Y. ; Sallaberry, A. ; Klein, K. ; Takatsuka, M. ; Roche, M. , "SentiCompass:Interactive visualization for exploring and comparing the sentiments of time-varying twitter data," Visualization Symposium (PacificVis), 2015 IEEE Pacific , vol. , no. , pp. 129-133,14-17 April 2015
  2. Brisson, L. ; Torrel, J. -C. , "Opinion mining on experience feedback: A case study on smartphones reviews," in Research Challenges in Information Science (RCIS), 2015 IEEE 9th International Conference on, vol. , no. , pp. 187-192, 13-15 May 2015
  3. Ramanujam, R. S. ; Nancyamala, R. ; Nivedha, J. ; Kokila, J. , "Sentiment analysis using big data," Computation of Power, Energy Information and Commuincation (ICCPEIC), 2015 International Conference on, vol. , no. , pp. 0480-0484, 22-23 April 2015
  4. P. Gamallo and M. Garcia. Citius: A naive-bayes strategy for sentiment analysis on english tweets. In Proceedings of International Workshop on Semantic Evaluation 2014, pages 171–175, Aug 2014.
  5. R. S. Dudhabaware ; M. S. Madankar, "Review on natural language processing tasks for text documents," Computational Intelligence and Computing Research (ICCIC), 2014 IEEE International Conference on, Coimbatore, pp. 1-5. doi: 10. 1109/ICCIC. 2014. 7238427
  6. F. L. d. Santos and M. Ladeira, "The Role of Text Pre-processing in Opinion Mining on a Social Media Language Dataset," Intelligent Systems (BRACIS), 2014 Brazilian Conference on, Sao Paulo, 2014, pp. 50-54
  7. L. Zhang, W. Xu and S. Li, "Aspect identification and sentiment analysis based on NLP," Network Infrastructure and Digital Content (IC-NIDC), 2012 3rd IEEE International Conference on, Beijing, 2012, pp. 660-664. doi: 10. 1109/ICNIDC. 2012. 6418838
  8. Bingwei Liu, E. Blasch, Yu Chen, Dan Shen and Genshe Chen, "Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier," Big Data, 2013 IEEE International Conference on, Silicon Valley, CA, 2013, pp. 99-104. doi: 10. 1109/BigData. 2013. 6691740
  9. K. Ghag; K. Shah,"Comparative analysis of the techniques for Sentiment Analysis", Department of Information Technology, MET's Shah & Anchor Kutchhi Engineering College, Mumbai 400706, India","Advances in Technology and Engineering (ICATE), 2013 International Conference on","20130606","2013
  10. Efstratios Kontopoulos, Christos Berberidis, Theologos Dergiades, Nick Bassiliades, Ontology-based sentiment analysis of twitter posts, Expert Systems with Applications, Volume 40, Issue10, August 2013, Pages 4065-4074, ISSN 0957-4174
  11. Lin E. ,Shiaofen Fang; Jie Wang, "Mining Online Book Reviews for Sentimental Clustering," in Advanced Information Networking and Applications Workshops (WAINA),2013 27th International Conference, vol. , no. , pp. 179-184, 25-28 March 2013
  12. J. Kottmann, B. Margulies, G. Ingersoll et al. , "Apache opennlp. The apache software foundation," 2013.
  13. Yingcai Wu; Furu Wei; Shixia Liu; Au, N. ; Weiwei Cui; Hong Zhou; Huamin Qu, "OpinionSeer: Interactive Visualization of Hotel Customer Feedback," in Visualization and Computer Graphics, IEEE Transactions on , vol. 16, no. 6, pp. 1109-1118, Nov. -Dec. 2010
  14. E. Cambria, R. Speer, C. Havasi, and A. Hussain, "Senticnet: A publicly available semantic resource for opinion mining. " in AAAI fall symposium: common sense knowledge, vol. 10, 2010, p. 02.
  15. A. Neviarouskaya, H. Prendinger, M. Ishizuka, SentiFul: Generating a reliable lexicon for sentiment analysis, Proceedings of the affective computing and intelligent interaction and workshops (ACII 2009), 3rd international conference on affective computing and intelligent interaction and workshops, IEEE (2009), pp. 10–12 September 1–6arning (EMNLP-CoNLL) (pp. 1075–1083)
  16. Ding, X. , Liu, B. and Yu, P. A Holistic Lexicon-Based Approach to Opinion Mining. Proceedings of the first ACM International Conference on Web search and Data Mining(WSDM'08), 2008.
  17. Kaji, N. , & Kitsuregawa, M. (2007). Building lexicon for sentiment analysis from massive collection of HTML documents. In Proceedings of the joint conference on empirical methods in natural language processing and computational natural language le
  18. M. Koppel and 1. SchIer (2005) "The Importance of Neutral Examples for Learning Sentiment". In IJCAI
  19. P. R. R. White; J. R. Martin, The Language of Evaluation: Appraisal in English. London/New York: Palgrave Macmillan, 2005.
  20. Hu, M and Liu, B. "Mining and Summarizing Customer Reviews". Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04), 2004.
  21. Tetsuya Nasukawa, Jeonghee Yi, Sentiment analysis: capturing favorability using natural language processing, Proceedings of the 2nd international conference on Knowledge capture, October 23-25, 2003, Sanibel Island, FL, USA
  22. M. M. Bradley; P. J. Lang. "Affective norms for english words (ANEW): Instruction manual and affective ratings. " Technical report, The Center for Study of Emotion and Attention, University of Florida, 1999.
  23. L. Feldman Barrett; J. A. Russell. "Independence and bipolarity in the structure of current affect. " Journal of personality and social psychology, 74(4):967–984, 1998.
  24. SentiWordeNet , http://sentiwordnet. isti. cnr. it/
  25. Vineeth G. Nair. 2014. Getting Started with Beautiful Soup. Packt Publishing.
  26. Perkins, J. (2010) Python text processing with NLTK 2. 0 Cookbook,Packt Publishing. Beatrice Santorin "Part-Of-Speech Tagging Guidelines for the Penn Treebank Project (3rd Revision)", University of Pennsylvania.
Index Terms

Computer Science
Information Sciences


Opinion Mining Sentiment Analysis Big Data Data Visualization Customer Reviews