Sentiment Analysis of movie reviews: A new feature-based sentiment classification

Ketan Sarvakar¹ , Urvashi K Kuchara²

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.3 , pp.8-12, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.812

Online published on Jun 30, 2018

Copyright © Ketan Sarvakar, Urvashi K Kuchara . 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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Abstract :
Sentiment Analysis also known as opinion mining is the task of detecting, extracting and classifying opinions or sentiments related to different topics. One such area of interest is sentiment classification or polarity determination of movie reviews in user specific choice which are dependent on either mood or emotion of user perspectives. This plays an important role in today’s world where the promotion of nay product or movies. Polarity determination is an important task for both the user and producer. They can take appropriate decision based on these results of classification. Thus, considering the needs and developing interests in social data mining and increasing dependency of users on customer reviews here we proposed a method to classify the data more accurately by altering the pre-processing tasks mainly filtering. Proposed methodology will be used for the available classification techniques by using the available dataset more consistently by working on the dictionary built by the filters.

Key-Words / Index Term :
Sentiment Analysis; Dictionary; tokenizer; Accuracy; StringToWordVector, CustomStringToWordVector filter

References :
[1] K. Ravi, V. Ravi , "A survey on opinion mining and sentiment analysis: Tasks, approaches and applications," Knowledge-Based Syst., Vol. 89, pp. 14–46, 2015.
[2] S. V. B. Pang, L. Lee, "Thumbs up? Sentiment Classification using machine learning techniques," ACL-02 Conf. Empir. Methods Nat. Lang. Process., Vol. 10, pp. 79–86, 2002.
[3] Dr. S. Vijayarani, Ms. R. Janani, " TEXT MINING: OPEN SOURCE TOKENIZATION TOOLS – AN ANALYSIS," Advanced Computational Intelligence: An International Journal (ACII), Vol. 3, No. 1, 2016.
[4] Ms. Anjali Ganesh Jivani, "A Comparative Study of Stemming Algorithms," Anjali Ganesh Jivani et al, Int. J. Comp. Tech. Appl., Vol. 2 (6), pp. 1930-1938.
[5] Nan Hu, Noi Sian Koh, Srinivas K. Reddy, "Ratings lead you to the product, reviews help you clinch it? The mediating, " Decision Support Systems, Vol. 57, pp. 42–53, 2014.
[6] Diana Maynard, Ian Roberts, Mark A. Greenwood, Dominic Rout, Kalina Bontcheva, "A framework for real-time semantic social media analysis," Web Semantics: Science, Services and Agents on the World Wide Web, 2017.
[7] Manajit Chakraborty, Sukomal Pal, Rahul Pramanik, C. Ravindranath, "Recent developments in social spam detection and combating techniques: A survey," Information Processing and Management, Vol. 52, pp. 1053–1073, 2016.
[8] Donato Hernández Fusilier, Manuel Montes-y-Gómez b, Paolo Rosso c, "Detecting positive and negative deceptive opinions using," Information Processing and Management, Vol. 51, pp. 433–443, 2015.
[9] Peter F. Brown, Peter V. deSouza*, Robert L. Mercer,V. Pietra, J. C. Lai, "Class-Based n-gram Models of Natural," IBM T. J. Watson Research Center .
[10] Asmita Dhokrat, Sunil Khillare, C. Namrata Mahender, "International Journal of Computer Applications Technology and Research - Review on Techniques and Tools used for Opini on Mining," International Journal of Computer Applications Technology and Research, Vol. 4, Issue 6, pp. 419 - 424.
[11] Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, "Classifier Chains for Multi-label Classificatio,"@cs.waikato.ac.nz in machine learning, 2011.
[12] Jeonghee Yi, Tetsuya Nasukawa, Razvan Bunescu, Wayne Niblack., "Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques," Proceedings of the Third IEEE International Conference on Data Mining (ICDM’03) , Vol. 0-7695-1978-4/03.
[13] J. H. Zaragoza, L. E. Sucar, E. F. Morales, C. Bielza, and P. Larranga, "Bayesian chain classifiers for multidimensional classification," In the proceedings of 2011 IJCAI Int. Jt. Conf. Artif. Intell., pp. 2192–2197, 2011.
[14] S. ChandraKala and C. Sindhu, “Opinion Mining and Sentiment Classification: A Survey,” ICTACT JOURNAL ON SOFT COMPUTING, Vol. 03, Issue. 01, 2012.