Polarity Classification Using Twitter Data?

Polarity Classification over Twitter offers different organizations a fast and effective way to monitor the feelings/emotions of general public towards their brand, business, politicians etc. A wide range of features for training polarity classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach for automatically classifying and adding semantics as additional features the polarity of Twitter messages. These messages are classified as positive or negative or neutral with respect to a query term. The paper focuses on addressing polarity classification for product features in product reviews by building semantic association between product features and polarity words. The results show that our method is encouraging.