  Our day-to-day life has always been influenced by what people think. Ideas
and opinions of others have always affected our own opinions. The explosion of
Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging,
Contributing to RSS, Social Bookmarking, and Social Networking. As a result
there has been an eruption of interest in people to mine these vast resources
of data for opinions. Sentiment Analysis or Opinion Mining is the computational
treatment of opinions, sentiments and subjectivity of text. In this report, we
take a look at the various challenges and applications of Sentiment Analysis.
We will discuss in details various approaches to perform a computational
treatment of sentiments and opinions. Various supervised or data-driven
techniques to SA like Na\"ive Byes, Maximum Entropy, SVM, and Voted Perceptrons
will be discussed and their strengths and drawbacks will be touched upon. We
will also see a new dimension of analyzing sentiments by Cognitive Psychology
mainly through the work of Janyce Wiebe, where we will see ways to detect
subjectivity, perspective in narrative and understanding the discourse
structure. We will also study some specific topics in Sentiment Analysis and
the contemporary works in those areas.
