Open Access   Article

Social Media Sentiment Analysis For Malayalam

M. Rahul1 , R.R. Rajeev2 , S. Shine3

Section:Research Paper, Product Type: Journal Paper
Volume-06 , Issue-06 , Page no. 48-53, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.4853

Online published on Jul 31, 2018

Copyright © M. Rahul, R.R. Rajeev, S. Shine . 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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Citation

IEEE Style Citation: M. Rahul, R.R. Rajeev, S. Shine, “Social Media Sentiment Analysis For Malayalam”, International Journal of Computer Sciences and Engineering, Vol.06, Issue.06, pp.48-53, 2018.

MLA Style Citation: M. Rahul, R.R. Rajeev, S. Shine "Social Media Sentiment Analysis For Malayalam." International Journal of Computer Sciences and Engineering 06.06 (2018): 48-53.

APA Style Citation: M. Rahul, R.R. Rajeev, S. Shine, (2018). Social Media Sentiment Analysis For Malayalam. International Journal of Computer Sciences and Engineering, 06(06), 48-53.

           

Abstract

Sentiment analysis or opinion mining is a Natural Language Processing to find the emotions of public opinion from user generated text. Sentiment Analysis in social media, acquiring large importance today because people use social media platforms to share their views and opinions on relevant topics in the form of movie reviews, product reviews, political discussions etc. The user generated text collected from social media can help machines to summarize and take intelligent decisions in different domains. Sentiment analysis in Malayalam language has a large importance. Malayalam is a low-resource language and it does not possess a standard corpus or a sentiment lexicon. This work presents a machine learning approach to sentiment analysis in Malayalam language using the CRF and SVM. The learning carried out at two levels and the system classify sentences into positive, negative and neutral classes. The work includes creation of a large size annotated corpus as a primary task and then followed by training a sentence level classifier to perform sentiment analysis.

Key-Words / Index Term

Sentiment Analysis, CRF, SVM, NLP

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