In social media messaging, Indian languages are frequently employed. Tamil is an ancient language that has been utilised frequently in tweets. Sentiment Analysis (SA) is an interdisciplinary field that encompasses both text mining and natural language processing. Sentiment analysis has advanced tremendously in recent years, particularly for the English language. However, there has been relatively little sentiment analysis work done for Indian languages such as Hindi, Tamil, Kannada, and others. The focus of this chapter focuses on Tamil tweets in order to determine the emotion of Tamil movie reviews. It is critical to examine the Tamil language content of tweets in order to gain a sense of the opinions stated in the tweets. The goal is to use Tamil SentiWordNet to classify the sentiment of Tamil films based on Tamil tweets (TSWN). The sentiment polarity of the Tamil movie dataset is determined using the Term Frequency - Inverse Document Frequency (TF-IDF) approach. The fundamental sentiment categorization of Tamil films is determined using domain specific ontology. In contextual semantics, the sentiment of a word can change depending on the word next to it. In this study, sentiment-bearing phrases and their neighbours in Tamil tweets are assessed using contextual semantic sentiment analysis to produce a more accurate outcome for movie sentimental classification.
Author(S) Details
Vallikannu Ramanathan
Department of Computer Science, Alagappa University, Karaikudi, India.
T. Meyyappan
Department of Computer Science, Alagappa University, Karaikudi, India.
S. M. Thamarai
Department of Computer Science, Alagappa Government Arts College, Karaikudi, India.
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