CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis

by Bhumika M. Jadav, Vimalkumar B. Vaghela
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 13
Year of Publication: 2016
Authors: Bhumika M. Jadav, Vimalkumar B. Vaghela
10.5120/ijca2016910921

Bhumika M. Jadav, Vimalkumar B. Vaghela . Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis. International Journal of Computer Applications. 146, 13 ( Jul 2016), 26-30. DOI=10.5120/ijca2016910921

@article{ 10.5120/ijca2016910921,
author = { Bhumika M. Jadav, Vimalkumar B. Vaghela },
title = { Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 13 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 26-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number13/25459-2016910921/ },
doi = { 10.5120/ijca2016910921 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:22.469566+05:30
%A Bhumika M. Jadav
%A Vimalkumar B. Vaghela
%T Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 13
%P 26-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social media is a popular network through which user can share their reviews about various topics, news, products etc. People use internet to access or update reviews so it is necessary to express opinion. Sentiment analysis is to classify these reviews based on its opinion as either positive or negative category. First we have preprocessed the dataset to convert unstructured reviews into structured form. Then we have used lexicon based approach to convert structured review into numerical score value. In lexicon based approach we have preprocessed dataset using feature selection and semantic analysis. Stop word removal, stemming, POS tagging and calculating sentiment score with help of SentiWordNet dictionary have been done in preprocessing part. Then we have applied classification algorithm to classify opinion as either positive or negative. Support vector machine algorithm is used to classify reviews where RBF kernel SVM is modified by its hyper parameters which are soft margin constant C , Gamma γ. So optimized SVM gives good result than SVM and naïve bayes. At last we have compared performance of all classifier with respect to accuracy.

References
  1. Liu, B., 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), pp.1-167.
  2. Y. Singh, P. K. Bhatia, and O. Sangwan, “A review of studies on machine learning techniques,” International Journal of Computer Science and Security, vol. 1, no. 1, pp. 70–84, 2007.
  3. Pang, B., Lee, L. and Vaithyanathan, S., 2002, July. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.
  4. Mouthami, K., Devi, K.N. and Bhaskaran, V.M., 2013, February. Sentiment analysis and classification based on textual reviews. In Information Communication and Embedded Systems (ICICES), 2013 International Conference on (pp. 271-276). IEEE.
  5. Go, A., Bhayani, R. and Huang, L., 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1, p.12.
  6. Kanakaraj, M. and Guddeti, R.M.R., 2015, February. Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques. In Semantic Computing (ICSC), 2015 IEEE International Conference on (pp. 169-170). IEEE.
  7. Chaovalit, P. and Zhou, L., 2005, January. Movie review mining: A comparison between supervised and unsupervised classification approaches. In System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 112c-112c). IEEE.
  8. Zhou, X., Tao, X., Yong, J. and Yang, Z., 2013, June. Sentiment analysis on tweets for social events. In Computer Supported Cooperative Work in Design (CSCWD), 2013 IEEE 17th International Conference on (pp. 557-562). IEEE.
  9. Shahana, P.H. and Omman, B., 2015. Evaluation of Features on Sentimental Analysis. Procedia Computer Science, 46, pp.1585-1592.
  10. Gautam, G. and Yadav, D., 2014, August. Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In Contemporary Computing (IC3), 2014 Seventh International Conference on (pp. 437-442).IEEE.
  11. Tripathy, A., Agrawal, A. and Rath, S.K., 2015. Classification of Sentimental Reviews Using Machine Learning Techniques. Procedia Computer Science,57, pp.821-829.
  12. Miller, G.A., 1995. WordNet: a lexical database for English. Communications of the ACM, 38(11), pp.39 41.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis Text mining SentiWordNet SVM Naïve Bayes RBF kernel SVM

Powered by PhDFocusTM