Analyzing Sentiment in Tamil Tweets using Deep Neural Network

Sentiment Analysis (SA) is the process of extracting the information from the given text in which the text consists of various emotions such as happy, sad, proud, fear and so on about various events, people, system, and facts. Since the social media is burgeoned with huge amount of data, it is necessary to derive some useful information from the available data. This extracted useful information can be used in product purchase, politics, sports, and more. To be precise, it helps in decision making. For implementing the sentiment analysis, a lot of research work has been carried out which ranges from lexicon based approach to machine learning based approach. In recent days deep learning and neural networks plays a major role in sentiment analysis and it is considered as a state-of-the art method for analysing various languages. Tamil is one of the Indian languages which still requires state-of-art model for sentiment analysis. The language specific features, grammar structure, agglutinative nature of Tamil language poses more challenges. In this paper, we proposed a combined character based Deep Bidirectional long short-term memory neural networks (DBLS TM) to analyse the Tamil tweets.