Social media sites are one of the most popular destinations in today’s online world. With millions of users visiting social networking sites like Facebook, YouTube, Twitter etc. every day to share social content at their disposal; from simple textual information about what they are doing at any moment of time, to opinions regarding products, people, events, movies to videos and music, these sites have become massive sources of user generated content. In this work we focus on one such social networking site - Twitter, for the task of information extraction and sentiment analysis.
This work presents a linguistic framework that first performs syntactic normalization of tweets on top of traditional data cleaning, extracts assertions from each tweet in the form of binary relations, and creates a contextualized knowledge base (KB). We then present a Language Model (LM) based classifier trained on a small set of manually tagged corpus, to perform sentence level sentiment analysis on the collected assertions to eventually create a KB that is backed by sentiment values. We use this approach to implement a contextualized sentiment based yes/no question answering system.