@article {10.3844/jcssp.2019.1638.1647,
article_type = {journal},
title = {Predicting Tamil Movies Sentimental Reviews Using Tamil Tweets},
author = {Ramanathan, Vallikannu and Meyyappan, T. and Thamarai, S.M.},
volume = {15},
number = {11},
year = {2019},
month = {Nov},
pages = {1638-1647},
doi = {10.3844/jcssp.2019.1638.1647},
url = {https://thescipub.com/abstract/jcssp.2019.1638.1647},
abstract = {Recently people are more frequently using their mother tongue to express their opinion and view in the social media. Especially Indian languages are often used in social media messages. Tamil is one of the oldest language which has been used slightly higher percentage in micro blogs. Sentiment analysis has gained incredible development in recent times mostly for English language. However very less work of sentiment analysis has done for Indian languages like Hindi, Tamil, Kannada etc., In this paper we focus on Tamil language tweets. It is essential to analyse the Tamil language content for tweets and get perception of opinion expressed by the tweets. Our objective is to classify the sentiment of the Tamil movies based on Tamil tweets using Tamil SentiWordNet (TSWN). We proposed Term Frequency - Inverse Document Frequency (TF-IDF) method to find the sentiment polarity of the Tamil movie dataset. This method provides baseline for our research. Domain specific ontology is used to identify the primary sentiment categorization of the Tamil movies. In contextual semantic, the sentiment of a word may flip based on the neighbouring word. In this research, sentiment-bearing terms and its neighbouring terms in Tamil tweets are evaluated using contextual semantic sentiment analysis to get more accurate result for the movie sentimental classification.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}