Open Access   Article


Binita Verma1 , Ramjeevan Singh Thakur2 , Shailesh Jaloree3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-10 , Page no. 28-34, Oct-2018


Online published on Oct 31, 2018

Copyright © Binita Verma, Ramjeevan Singh Thakur, Shailesh Jaloree . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Binita Verma, Ramjeevan Singh Thakur, Shailesh Jaloree, “PREDICTING SENTIMENT FROM MOVIE REVIEWS USING LEXICON BASED MODEL”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.28-34, 2018.

MLA Style Citation: Binita Verma, Ramjeevan Singh Thakur, Shailesh Jaloree "PREDICTING SENTIMENT FROM MOVIE REVIEWS USING LEXICON BASED MODEL." International Journal of Computer Sciences and Engineering 6.10 (2018): 28-34.

APA Style Citation: Binita Verma, Ramjeevan Singh Thakur, Shailesh Jaloree, (2018). PREDICTING SENTIMENT FROM MOVIE REVIEWS USING LEXICON BASED MODEL. International Journal of Computer Sciences and Engineering, 6(10), 28-34.

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Large number of users shares their opinion on social networking sites. So, on the web an enormous quantity of data is generated daily. Usually there is not enough human resource to examine this data. The methods for automatic opinion mining on online data are becoming increasingly. From the past few years, methods have been developed that can successfully analyze the sentiment from digital text. These developments enable research into prediction of sentiment. Sentiment prediction has been used as a tool for movie review prediction. The aim of this work is to explore the use of lexicons to extract the sentiment prediction for a number of movie reviews. In this paper, a comparative analysis of lexicon based models has to predict the sentiments of movie reviews dataset together with evaluation metrics.

Key-Words / Index Term

Movie reviews, Lexicon based model, Predicting sentiment


[1] S. Vishal A. Kharde and S.S Sonawane, “Sentiment analysis of Twitter data: A survey of Techniques”, International Journal of Computer Applications, Pp.975-8887, vol. 139 No.11, April 2016.
[2] Pushpendra Kumar and Ramjeevan Singh Thakur, “Recommendation system techniques and related issues: a survey”, International Journal of Information Technology, Vol.10 (4), pp. 495–501, 2018.
[3] Vinod Kumar, Pushpendra Kumar and R.S. Thakur, “A brief Investigation on Data Security Tools and Techniques for Big Data”, International Journal of Engineering Science Invention, Vol. 6(9), PP. 20-27, 2017.
[4] Pushpendra Kumar and R. S. Thakur, “A Framework for Weblog Data Analysis Using HIVE in Hadoop Framework”, In: Proceedings of International Conference on Recent Advancement on Computer and Communication, Lecture Notes in Networks andSystems 34,(2018),
[5] C. Musto, G. Semeraro and M. Polignano, “A Comparison of Lexicon based approaches for Sentiment Analysis of microblog posts”, International Workshop on Information Filtering and Retrieval, Pisa, Italy, Dec 2014.
[6] K. Lerman, A. Gilder, M. Dredze and F. Pereira, “Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis”, In 22nd International Conference on Computational Linguistics, Manchester, UK, pp. 473-480, 2008.
[7] R. Balasubramanyan, W.W.Cohen, D. Pierce and D. P. Redlawsk, “What pushes their buttons? Predicting Comment Polarity from the content of Political blog posts”, In Workshop on Language in Social Media, USA, 2011.
[8] R. Balasubramanyan, W. Cohen, D. Pierce and D. Redlawsk, "Modeling Polarizing Topics: When do different Political Communities respond differently to the same news", In 6th International AAAI Conference on Weblogs and Social Media, Dublin, Ireland, 2012.
[9] G. P. C. Fung, J. X. Yu and W. Lam, "News Sensitive Stock Trend prediction", In Advances in Knowledge Discovery and Data Mining : 6th Pacific-Asia Conference, Taipel, Taiwan, pp. 481-493, 2002.
[10] G. Pui Cheong Fung, J. Xu Yu and Wai Lam, "Stock prediction: Integrating text mining approach using real-time news”, IEEE International Conference on Computational Intelligence for Financial Engineering, pp. 395-402, 2003.
[11] V. Sehnal and C. Song, “SOPS: Stock Prediction using Web Sentiment”, In Seventh IEEE 77 International Conference on Data Mining Workshops, USA, pp. 21-26, 2007.
[12] Y. Marchand, V. Keselj, E. Milios and M. Shepherd, “Quantifying the role of the Opinion Lexicon in Sentiment”, Symposium and Workshop on Measuring Influence on Social Media, 2012.
[13] T. Nasukawa and J. Yi, “Sentiment Analysis: Capturing favorability using Natural Language Processing”, In Proceedings of the 2nd International Conference on Knowledge capture, Pp. 70–77, ACM, 2003.
[14] N. Medagoda, S. Shanmuganathan and J.Whalley, “A Comparative Analysis Of Opinion Mining And Sentiment Classification In NonEnglish Languages”, IEEE International Conference on Advances in ICT for Emerging Regions (ICTer), Pp. 144 – 148, 2013.
[15] R. Varghese and M. Jayashree, “Aspect based sentiment analysis using support vector machine classifier”, Advances in Computing, Communications and Informatics (ICACCI)), International Conference on IEEE, Pp. 1581–1586, 2013.
[16] B. Ohana and B. Tierney, “Sentiment Classification of reviews using SentiWordNet”, In 9th. IT & T Conference, Pp. 13, 2009.
[17] A. Khan, B. Baharudin and K. Khan, “Sentence based Sentiment Classification from Online customer reviews, FIT , 2010.
[18] B. Baharum, H. L. Lam and K. Khairullah, "A Review of Machine Learning Algorithms for Text-Documents Classification," Journal of Advances in Information Technology (JAIT), Vol. 1, no. 1, pp. 4-20, Feburary 2010.
[19] W. Medhat, A. Hassan and H. Korashy, “Sentiment Analysis Algorithms and Applications: A Survey”, Ain Shams Engineering Journal, Vol 5, Issue 4, Pp. 1093-1113, 2014.
[20] M. Taboada, J. Brooke, M. Tofiloski, K. Voll and M. Stede, “Lexicon- based methods for Sentiment Analysis Computational linguistics”, Vol 32, Issue 2, PP. 267-307, 2011.
[21] E. Younis, “Sentiment Analysis and Text Mining for Social Media Microblogs using Open Source Tools: An Empirical Study”, International Journal of Computer Applications, Vol. 112, No. 5, pp. 0975-8887, February 2015.
[22] Esuli Andrea and Sebastiani Fabrizio, “SentiWordNet: A publicly available Lexical resource for Opinion Mining”, In Proceedings of Language Resources and Evaluation (LREC), 2006.
[23] F. Arup Nielsen, “A New ANEW: Evaluation of a word list for sentiment analysis in microblogs”, In Proceedings of the 1st Workshop on Making Sense of Microposts , Pp. 93–98, 2011.
[24] C.J. Hutto and Eric Gilbert, “ VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text”, Eight International Conference on Weblogs and social Media, 2014.
[25] Andrew Lee Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng and Christopher Potts, ”Learning WordVectors for Sentiment Analysis”, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Vol 1, Pp. 142-150, 2011.
[26] Rafael M., D’Addio , Marcos A., Domingues, Marcelo G., and Manzato, “Exploiting feature extraction techniques on users reviews for movies recommendation”, Journal of the Brazillian Computer Society, Vol.23, Pp-7, 2017.
[27] Kushal Dave, Steve Lawrence and David M. Pennock , “Mining the Peanut gallery: Opinion extraction and Semantic Classification of product reviews” , In Proceedings of WWW 2003, Pp. 519-528, 2003.
[28] G. Vinodhini and R. Chandrasekaran, “Sentiment Analysis and Opinion Mining : A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue 6, june 2012.
[29] I.V. Shravan, “Sentiment Analysis in Python using NLTK”, OSFY- Open Source For You, 2016.