Social networking sites like twitter have millions of people share their thoughts day by day as tweets. As tweet is characteristic short and basic way of expression.There are a number of social networking sites and interrelated mobile applications, and some more are still rising. An enormousquantity of data is generated by these sites daily and this data can be used as a source for differentexamination purposes. People interrelate with each other; share their ideas, opinions, interests and personal information. These user tweet are used for finding the sentiments and also add financial, commercial and social values. though, due to the enormous quantity of user-generated information, analyzing the information manually is an expensive method. Increasing sentiment analysis activity, challenges are being added every day. Automated analytical methods are needed to extract views transmitted in user remarks. Opinion mining is the computational analysis of views transmitted in natural language for decision-making purposes. Preprocessing data play a vital role in getting accurate sentiment analysis results. Extracting opinion target words provide fine-grained analysis on the customer twwets. The labeled data required for training a classifier is expensive and hence to overcome, This paper shows opinion mining analysis types and techniques used to perform extraction of opinions from tweets. A Comparative study on the different techniques and approaches of opinion mining twitter data are dealt with in this survey paper.

Sentiment Analysis, Opinion Mining, Social Media, Twitter Data

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