Sentiment Detection from Punjabi Text using Support Vector Machine
Keywords:
Sentiment Analysis, Natural Language Processing, Punjabi News Articles, Support Vector MachineAbstract
This paper focuses on sentiment analysis on Punjabi News Articles using Support Vector Machine. Sentiment analysis is a field of Natural Language Processing and it is the most trending field of research. Sentiment analysis on Punjabi language is to be performed because of increasing amount of Punjabi content over the web, provides an important aspect for the researchers, organizations, and governments to analyze the user-generated content and get the useful information from it. With the increase in the amount of information being communicated via regional languages like Punjabi, comes a promising opportunity of mining this information. Support Vector Machine approach is used by proposed system to classify the content. Support Vector Machine is a supervised machine learning approach that is be used for classification and regression problems. However, it is mostly used for classification problems. So there is a need to analyze the Punjabi language content and get better understanding of Punjabi text. The work focuses on detecting positive or negative sentiment from Punjabi content. The results of the proposed system depict remarkable accuracy. The accuracy of sentiment analysis on Punjabi news articles using Support vector machine is found to be 90%.
References
P. Panday, “A survey of Sentiment classification techniques used for Indian regional languages ,” International Journal on Computational Science and Applications, vol. 5, pp. 13-26, April 2015.
A. Balahur and G. Jacquet, “Sentiment analysis meets social media – Challenges and solutions of the field in view of the current information sharing context,” International Journal Of Engineering And Computer Science, vol. 3, pp. 428–432, July 2015.
J. Kaur and R. Saini, “A study and analysis of opinion mining research in indo-aryan, dravidian and tibeto-burman language families,” International Journal of Data Mining and Emerging Technologies, vol. 4, no. 2, pp. 53-60, July 2014.
A. Kaur and V. Gupta, “Proposed algorithm of sentiment analysis for Punjabi text,” International Journal of Science and Technology, vol. 6, no. 4, pp. 180-183, May 2014.
A. Sharma, “Sentiment analyzer using Punjabi language,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 9, pp. 5904-5905, September 2014.
R. Prabowo and M. Thelwall, “Sentiment analysis: a combined approach,” International Journal of Computer Applications & Information Technology, vol. 4, no. 8, pp. 143-157, July 2009.
A. Das and S. Bandyopadhyay, “Opinion-Polarity Identification in Bengali,” International Arab Journal of Information Technology, vol. 2, no. 9, pp. 169-182, May 2010.
K. Mouthami and A. Baloglu, “Sentiment analysis and classification based on textual reviews,” International Conference on Internet and Web Applications and Services, chicago, 2010, pp. 10-17.
N.C. Troussas, and A. Agarwal, “Sentiment analysis in twitter using machine learning techniques,” International Journal of Computer Science and Engineering, vol. 4, no. 2, p. 3038, June 2011.
N. Garg “Movie review mining in Punjabi,” International Journal of Application or Innovation in Engineering & Management, vol. 2, no. 12, pp. 372-375, December 2013.
J. Kaur, J. Kumar, and R. SAINI, "A study of text classification natural language processing algorithms for Indian languages,” International Journal of Computer Applications & Information Technology, vol. 4, no. 9, pp. 162-167, July 2015.
A. Das, “Opinion-polarity identification in Bengali,” International Journal of Computer Science, vol. 10, no. 5, pp. 169-182, April 2010.
W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 1, no. 5, pp. 1093-1113, April 2014.
J. Bhaskar, K. Sruthi, and P. Nedungadi, "Hybrid approach for emotion classification of audio conversation based on text and speech mining,” International Conference on Information and Communication Technologies, Amritapuri, pp. 635-643, April 2015.
R. Kumar and R. Vadlamani , “A survey on opinion mining and sentiment analysis: tasks, approaches and applications,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 37, no. 11, pp. 14-46, June 2015.
V. Kumar, “Opinion mining and sentiment analysis,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 39, no. 10, pp. 141-166, April 2015.
U. Jain and A. Sandu, “Emotion Detection from Punjabi Text using Hybrid Support Vector Machine and Maximum Entropy Algorithm,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 11, p. 5, November 2015.
V.K. Singh and R. Rani, “Sentiment analysis of movie reviews for aspect-level sentiment classification,” International Journal of Advanced Computer Research, vol. 9, no. 5, pp. 10-39, April 2015.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
