CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Analysis of Various Sentiment Classification Techniques

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

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 3
Year of Publication: 2016
Authors: Vimalkumar B. Vaghela, Bhumika M. Jadav
10.5120/ijca2016909259

Vimalkumar B. Vaghela, Bhumika M. Jadav . Analysis of Various Sentiment Classification Techniques. International Journal of Computer Applications. 140, 3 ( April 2016), 22-27. DOI=10.5120/ijca2016909259

@article{ 10.5120/ijca2016909259,
author = { Vimalkumar B. Vaghela, Bhumika M. Jadav },
title = { Analysis of Various Sentiment Classification Techniques },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 3 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number3/24574-2016909259/ },
doi = { 10.5120/ijca2016909259 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:18.609300+05:30
%A Vimalkumar B. Vaghela
%A Bhumika M. Jadav
%T Analysis of Various Sentiment Classification Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 3
%P 22-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis is an ongoing research area in the field of text mining. People post their review in form of unstructured data so opinion extraction provides overall opinion of reviews so it does best job for customer, people, organization etc. The main aim of this paper is to find out approaches that generate output with good accuracy. This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms. The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.

References
  1. Liu, B., 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), pp.1-167.
  2. 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.
  3. 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.
  4. Miller, G.A., 1995. WordNet: a lexical database for English. Communications of the ACM, 38(11), pp.39-41.
  5. Tripathy, A., Agrawal, A. and Rath, S.K., 2015. Classification of Sentimental Reviews Using Machine Learning Techniques. Procedia Computer Science,57, pp.821-829.
  6. Shahana, P.H. and Omman, B., 2015. Evaluation of Features on Sentimental Analysis. Procedia Computer Science, 46, pp.1585-1592.
  7. Jeyapriya, A. and Selvi, K., 2015, February. Extracting aspects and mining opinions in product reviews using supervised learning algorithm. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on (pp. 548-552). IEEE.
  8. 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.
  9. 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.
  10. Bhadane, C., Dalal, H. and Doshi, H., 2015. Sentiment analysis: Measuring opinions. Procedia Computer Science, 45, pp.808-814.
  11. 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.
  12. 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.
  13. Go, A., Bhayani, R. and Huang, L., 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1, p.12.
  14. Khan, F.H., Qamar, U. and Javed, M.Y., 2014, November. Sentiview: A visual sentiment analysis framework. In Information Society (i-Society), 2014 International Conference on (pp. 291-296). IEEE.
  15. da Silva, N.F., Hruschka, E.R. and Hruschka, E.R., 2014. Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, pp.170-179.
  16. Michael W. Berry, Soft Computing in Data Science, First International Conference, Scds 2015, Putrajaya, Malaysia, September 2-3, 2015, Proceedings (Communications in Computer and Information Science)
  17. Patel, S.N. and Choksi, M.J.B., 2015. A Survey of Sentiment Classification Techniques. Journal for Research| Volume, 1(01).
  18. https://en.wikipedia.org/wiki/Part-of-speech_tagging
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis Text mining Classification Feature selection

Powered by PhDFocusTM