ABSTRACT
Sentiment Analysis (SA) is an active area of study in the field of text mining. SA is the computational treatment of thoughts, emotions and literary subjectivity. This paper addresses a detailed summary of the latest update in this area. The related fields of SA (transfer learning, emotional detection, and resource building) that attracted researchers have recently been explored. The paper provides a description of the various approaches to sentiment classification and methods used for sentiment analysis. Starting from this summary, the paper introduces a classification of methods with respect to features, advantages and limitations.
