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Review on sentiment analysis in Tamil texts

Authors:

Sajeetha Thavareesan ,

Eastern University, LK
About Sajeetha
Department of Mathematics
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Sinnathamby Mahesan

University of Jaffna, LK
About Sinnathamby
Department of Computer Science
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Abstract

Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to analyse the sentiments expressed in the text. It classifies into categories of qualities and opinions such as good, bad, positive, negative, neutral, etc. It employs machine learning techniques and lexicons for the classification. Nowadays, people share their opinions or feelings about movies, products, services, etc. through social media and online review sites. Analysing their opinions is beneficial to the public, business organisations, film producers and others to make decisions and improvements. SA is mostly employed in English language but rare for Indian languages including Tamil. This review paper aims to critically analyse the recent literature in the field of SA with Tamil text. Objectives, Methodologies and success rates are taken in consideration for the review. We shall conclude from the review that SVM and RNN classifiers taking TF-IDF and Word2vec features of Tamil text give better performance than grammar rules based classifications and other classifiers with presence of words, TF and BoW as features.
DOI: https://doi.org/10.4038/jsc.v9i2.14
How to Cite: Thavareesan, S. and Mahesan, S., 2018. Review on sentiment analysis in Tamil texts. Journal of Science, 9(2), pp.1–19. DOI: http://doi.org/10.4038/jsc.v9i2.14
Published on 31 Dec 2018.
Peer Reviewed

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