LEARNING BASED APPROACH FOR HINDI TEXT SENTIMENT ANALYSIS USING NAIVE BAYES CLASSIFIER

Sentiment analysis can be briefly described as the process of analyzing the emotion and opinion a particular sentence carries using natural language processing techniques. With the increase in the amount of information being communicated via regional languages like Hindi, 4th commonly spoken language in the world and its high potential for knowledge discovery comes a promising opportunity to apply sentiment analysis on this information. Hindi, being morphologically rich and free order language when contrasted with English, adds intricacy while dealing with the user-generated content. Most of the work in this domain has been done in the English language. This paper attempts to classify the polarities of the reviews or opinions expressed in the Hindi language into positive or negative sentiments using a supervised machine learning algorithm called Naïve Bayes Classifier and evaluate the overall model’s performance with respect to various parameters