Abstract Data has become the currency of this era and is continually increasing in size and value. The available data online is doubling in size very rapidly. The advancement of web technology and its growth has seen a huge volume of data present in the web for internet users. 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, discussion with different communities, or post messages across the world. There have been a number of researches in the field of sentiment analysis solely focusing on social media data. This survey centres mainly on tools and techniques used in sentiment analysis for analyzing information in social media where opinions are highly unstructured, heterogeneous and are positive, negative, or neutral in some cases. In this paper, we provide a survey and a comparative analysis of existing techniques and tools for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. This paper will in addition describe the study on different web resources such as review sites, blogs, discussion forums and news. It also gives overview of tools and application area of sentiment analysis.