  In this paper , we tackle Sentiment Analysis conditioned on a Topic in
Twitter data using Deep Learning . We propose a 2-tier approach : In the first
phase we create our own Word Embeddings and see that they do perform better
than state-of-the-art embeddings when used with standard classifiers. We then
perform inference on these embeddings to learn more about a word with respect
to all the topics being considered, and also the top n-influencing words for
each topic. In the second phase we use these embeddings to predict the
sentiment of the tweet with respect to a given topic, and all other topics
under discussion.
