  With the advancement of web technology and its growth, there is a huge volume
of data present in the web for internet users and a lot of data is generated
too. Internet has become a platform for online learning, exchanging ideas and
sharing opinions. Social networking sites like Twitter, Facebook, Google+ are
rapidly gaining popularity as they allow people to share and express their
views about topics,have discussion with different communities, or post messages
across the world. There has been lot of work in the field of sentiment analysis
of twitter data. This survey focuses mainly on sentiment analysis of twitter
data which is helpful to analyze the information in the tweets where opinions
are highly unstructured, heterogeneous and are either positive or negative, or
neutral in some cases. In this paper, we provide a survey and a comparative
analyses of existing techniques for opinion mining like machine learning and
lexicon-based approaches, together with evaluation metrics. Using various
machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector
Machine, we provide a research on twitter data streams.General challenges and
applications of Sentiment Analysis on Twitter are also discussed in this paper.

Computation and Language