Abstract
The World Wide Web has intensely evolved a novel way for people to express their views and opinions about different topics, trends and issues. The user-generated content present on different mediums such as internet forums, discussion groups, and blogs serves a concrete and substantial base for decision making in various fields such as advertising, political polls, scientific surveys, market prediction and business intelligence. Sentiment analysis relates to the problem of mining the sentiments from online available data and categorizing the opinion expressed by an author towards a particular entity into at most three preset categories: positive, negative and neutral. In this paper, firstly we present the sentiment analysis process to classify highly unstructured data on Twitter. Secondly, we discuss various techniques to carryout sentiment analysis on Twitter data in detail. Moreover, we present the parametric comparison of the discussed techniques based on our identified parameters.
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