#title We know tweeted last summer ! Shrey Gupta & Sonali Aggarwal

Aim of Project To build a user’s profile by examining his tweets. We try to solicit information regarding likes and dislikes of a user from his tweets. We do this by doing sentiment analysis on the tweets and then extracting the subject of like/dislike.

Strategy

User or Entity First Approach – use hand crafted features such as presence of links, pronouns, emoticons, % of correct words. Second Approach - build features entirely on the POS Tagger output. Used MART (Multiple Additive Regression Trees) for classification

Sentiment Analysis Aims to differentiate between a positive and negative sentiments in the tweet Our approach focuses on selecting indicative lexical features (e.g., the word -good, love, hate ), classifying a tweet according to the number and intensity of such features that occur anywhere within it and then using valence shifters to improve performance (for instance the word “not" before “good" reverses the polarity of the word “good").

Information Extraction We modified the Stanford Parser to specifically extract the verb phrase (activity) and other parts of the sentence containing the subject of the activity which the user likes or dislikes.