Various Approaches in Sentiment Analysis

In natural language processing (NLP), Sentiment analysis is flourishing rapidly as it aims at finding the emotional tone behind a piece of text and helps in understanding the attitudes, opinions, and emotions expressed within an online mention. The internet is flooded with large amounts of data such as texts, photos, audio, and video. With the help of sentiment analysis, we can find useful information from this data. This paper presents various approaches in sentiment analysis used by researchers and the major challenges faced in this field. By analyzing different papers, we came to know that the most widely used techniques in machine learning are Naïve Bayes and SVM and in NLP, the lexicon-based approach is used. With the advancement in technology, sentiment analysis will be available for the public and smaller companies as well.