Microblogging websites such as Twitter offer a wealth of insight into a population’s current mood. Automated approaches to identify general sentiment toward a particular topic often perform two steps: Topic Identification and Sentiment Analysis. Topic Identification first identifies tweets that are relevant to a desired topic (., a politician or event), and Sentiment Analysis extracts each tweet’s attitude toward the topic. Many techniques for Topic Identification simply involve selecting tweets using a keyword search. Here, we present an approach that instead uses distant supervision to train a classifier on the tweets returned by the search. We show that distant.
