Twitter is widely used as a microblogging service by users worldwide. The volumes of social networking messages and the short length of each message pose different challenges towards analysis of this data. We build a tweet analytics portal (TAP) to address these challenges. The key features of the portal are that it (i) curates the tweets, (ii) analyzes them to identify important and popular events, (iii) obtains cumulative sentiments associated with these events and (iv) analyzes the semantic content of tweets. The results of our analysis are presented visually in the form of tables and graphs.