Comparative Analysis of Different Supervised Machine Learning Algorithms for Sentiment Analysis

Gundeep Singh, Sukhmanjit Kaur, Prabhsimran Singh


With advancement in technology, sentiment analysis has seen an exponential growth in last few years. Sentiment analysis is natural language processing technique which deals with emotion detection from given text. A major reason for popularity of this field is the fact it is applied to large number of application domains. Due to this, various techniques have been proposed to perform sentiment analysis using supervised machine learning algorithms. But a major question arises, that which algorithm is the best. This paper tries to provide solution of this question, by performing a comparative analysis of various algorithms taking different datasets under consideration. Our results show that Naïve Bayes out performs all other algorithms.

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