Sentiment Analysis (SA) is the computational study of opinions, sentiments and emotions expressed in text in order to determine the thoughts of people in the direction of certain objects and facts. The opinions of people have a major influence in our every day decision-making process. In recent days, the people are sharing their opinions in the form of blogs, tweets, face book messages, news groups, comments and reviews. The proposed Dictionary Based Support Vector Machine Feature Selection (DBSVMFS) model extracts sentiment features using Support Vector Machine (SVM) weight method to improve the performance of SA. Different levels of pre-processing methods are applied to reduce the features. A set of sentiment features Adjectives, Adverbs and Verbs are extracted by using WordNet based POS (Part-Of-Speech). Feature selection using SVM weight method is applied to select the most important features. SVM classifier is used for sentiment classification and the experimental results prove the effectiveness of the proposed model by improving sentiment classification accuracy.

Sentiment Analysis, Classification, Support Vector Machine, Feature Selection, Part-Of- Speech

[1]. B. Pang, L. Lee, “Sentiment Analysis using Subjectivity Summarization Based on Minimum Cuts”, In the Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, 271–278, 2004.
[2]. T. Gautami, S. Naganna, “Feature Selection and Classification Approach for Sentiment Analysis”, Journal of Machine. Learning Applications, No.2, pp. 1-16, 2015.
[3]. Anuj Sharma, Shubhamoy Dey, “Performance Investigation of Feature Selection Methods and Sentiment Lexicons for Sentiment Analysis”, Special Issue of International Journal of Computer Applications (0975 – 8887) on Advanced Computing and Communication Technologies for HPC Applications - ACCTHPCA, pp. 15-20, June 2012.
[4]. Supaporn, Lonapalawong, Jun, Zhang Le, “Applying Relief Algorithm for Feature Selection in Sentiment Classification for Movie Reviews” Journal of Computational and Theoretical Nano Science, Volume 14, Number 11, pp. 5418-5423(6), November 2017.
[5]. Rajwinder Kaur , Prince Verma, “Sentiment Analysis of Movie Reviews: A Study of Machine Learning Algorithms with Various Feature Selection Methods”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-9 E-ISSN: 2347-2693,.pp.113-121, 2017.
[6]. A.S. Manek, P.D. Shenoy,M.C. Mohan and Venugopal K R, “Aspect term extraction for Sentiment Analysis in Large Movie Reviews using Gini Index Feature Selection Method and SVM Classifier”, World Wide Web Internet and Web Information Systems Springer, Volume 20, Issue 2, pp 135–154, 2016.
[7]. Shahana Bini Omman, “Evaluation of Features on Sentimental Analysis”, In the Proceedings of the International Conference on Information and Communication Technologies (ICICT 2014), Procedia Computer Science 46, pp. 1585 – 1592, 2015.
[8]. Pramod M. Mathapati , A.S. Shahapurkar , K.D.Hanabaratti, “Sentiment Analysis using Naïve bayes Algorithm”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-7, 2017.
[9]. Pang, B. & Lee. L, “Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales”, In the Proceedings of the 43rd annual meeting of the Association for Computational Linguistics (ACL), pp. 115–124, University of Michigan, USA, June 25–30, 2005.
[10]. Rohini S. Rahate, Emmanuel M, “Feature Selection for Sentiment Analysis by using SVM”, International Journal of Computer Applications, Volume 84, No 5, December 2013.
[11]. Supriya B. Moralwar1 , Sachin N. Deshmukh, “Different Approaches of Sentiment Analysis”, International Journal of Computer Sciences and Engineering, Volume-3, Issue-3, 2015.
[12]. Bing Liu, Sentiment Analysis and Opinion Mining, Morgan and Claypool Publishers, California, 2012.
[13]. Ciurumelea, Adelina, "Analyzing Reviews and Code of Mobile Apps for Better Release Planning", IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER),Austria, 2017.
[14]. Saeys, Yvan, Thomas Abeel, and Yves Van de Peer, "Robust Feature Selection using Ensemble Feature Selection Techniques." Machine Learning and Knowledge Discovery in Database, pp. 313-325, 2008.
[15]. Nurul Fathiyah Shamsudin, Halizah Basiron, Zurina Saaya, “Lexical Based Sentiment Analysis – Verb, Adverb & Negation”, Journal of Telecommunication, Electronic and Computer Engineering, ISSN: 2180 – 1843 e-ISSN: 2289-8131 , Vol. 8 No. 2, pp.161-166, 2017.
[16]. Oaindrila Das, Rakesh Chandra Balabantaray, “Sentiment Analysis of Movie Reviews using POS Tags and Term Frequencies”, International Journal of Computer Applications (0975 – 8887), Volume 96– No.2, June 2014.
[17]. B.M. Anitha, B.R. Bhargavi, “Opinion Classification Based on Verb, Adverb and Adjectives: Using Various Supervised Machine Learning Algorithms”, In: Multimedia Processing, Communication and Information Technology, ACEEE, pp. 236-242, 2013.
[18]. K. Bhuvaneswari and R. Parimala, “Sentiment Classification using Correlation and Instance Feature Selection”, International Journal of Pure and Applied Mathematics, Volume 118, No. 6, pp. 407-415. Special Issue, 2018