Indian Journal of Science and Technology

Abstract

Objectives: Sentiment analysis from the online web and social media contents is an important research and applications field for the organizations, businesses, and political and social life issues; in the business world sentiment analysis provides a clear picture of both quality and user satisfaction about the products, services or an event. Methods/ Statistical Analysis: Extraction of the information from the web, classification and prediction of the sentiment polarity is a complex process which performed through various approaches like Part-Of-Speech Tagging (POST), Support Vector Machine (SVM), and so on. In this paper, the efficient sentiment analysis schemes that introduced in the recent years are discussed and analyzed in order to understand the novel ideas behind these methodologies. Findings: This paper also highlights the advantages and disadvantages of the analyzed methodologies with the objective of determining the efficiency of the sentiment analysis schemes. Finally the sentiment analysis schemes have been compared in terms of performance evaluation metrics with respect to the social media contents. Thus this paper work provides a detailed analysis of the recent sentiment analysis schemes and throws light on new avenues for future research work in this domain. 

Keywords: POS Tagging, Sentiment Analysis, Social Media, Text Mining