Review on Sentiment Analysis in Natural Language Processing

Abstract

Sentiment analysis is text analysis techniques that automatically detect polarity of text. Sentiment analysis
also called as opinion mining which is one of the major tasks of NLP (Natural Language Processing). Sentiment
analysis has gain much attention in recent years. People are intended to develop a system that can identify and classify
opinion or sentiment as represented in an electronic text. Consumers regularly face the trade-off in purchase decisions so
nowadays if one wants to buy a consumer product one prefer user reviews and discussion in public forums on web about
the product. Many consumers use reviews posted by other consumers before making their purchase decisions. People
have a tendency to express their opinion on various entities. As a result opinion mining has gained importance. Sentiment
Analysis deals with evaluating whether this expressed opinion about the entity has a positive or a negative orientation.
Consumers need to decide what subset of available information to use. The process of identifying and extracting
subjective information from raw data is known as sentiment analysis. An accurate method for predicting sentiments could
enable us, to extract opinions from the internet and predict online customer’s preferences, which could prove valuable for
economic or marketing research. Till now, there are few different problems predominating in this research community,
namely, sentiment classification, feature based classification and handling negations. This paper presents a survey
covering the techniques and methods in sentiment analysis and challenges appear in the field.