Sentiment Estimation on Twitter

We study the classifier quantification problem in the context of the topical opinion retrieval, that consists in estimating proportions of the sentiment categories in the result set of a topic. We propose a methodology to circumvent individual classification allowing a real-time sentiment analysis for huge volumes of data. After discussing existing approaches to quantification, the novel proposed methodology is applied to Microblogging Retrieval and provides statistically significant estimates of sentiment category proportions. Our solution modifies Hopkins and King’s approach in order to remove manual intervention, and making sentiment analysis feasible in real time. Evaluation is conduced with a test collection made up of about 3,2M tweets.