Context-aware Models for Twitter Sentiment Analysis

Recent works on Sentiment Analysis over Twitter are tied to the idea that the sentiment can be completely captured after reading an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution of this contextual information is investigated for the detection of the polarity of tweet messages. We modeled the polarity detection problem as a sequential classification task over streams of tweets. A Markovian formulation of the Support Vector Machine discriminative model has been here adopted to assign the sentiment polarity to entire sequences. The experimental evaluation proves that sequential tagging better embodies evidence about the contexts and is able to increase the accuracy of the resulting polarity detection process. These evidences are strengthened as experiments are successfully carried out over two different languages: Italian and English. Results are particularly interesting as the approach is flexible and does not rely on any manually coded resources.