Sentiment Analysis for Social Media: A Survey

In the past years, the World Wide Web (WWW) has become a huge source of user-generated content and opinionative data. Using social media, such as Twitter, Facebook, etc. user share their views, feelings in a convenient way. Social media, such as Twitter, Facebook, etc, where millions of people express their views in their daily interaction, which can be their sentiments and opinions about particular thing. These ever-growing subjective data are, undoubtedly, an extremely rich source of information for any kind of decision making process. To automate the analysis of such data, the area of Sentiment Analysis has emerged. It aims at identifying opinionative data in the Web and classifying them according to their polarity, i.e., whether they carry a positive or negative connotation. Sentiment Analysis is a problem of text based analysis, but there are some challenges that make it difficult as compared to traditional text based analysis This clearly states that there is need of an attempt to work towards these problems and it has opened up several opportunities for future research for handling negations, hidden sentiments identification, slangs, polysemy. However, the growing scale of data demands automatic data analysis techniques. In this paper, a detailed survey on different techniques used in Sentiment Analysis is carried out to understand the level of work