Word clustering based on POS feature for efficient twitter sentiment analysis

With rapid growth of social networking service on Internet, huge amount of information are continuously generated in real time. As a result, sentiment analysis of online reviews and messages has become a popular research issue [1]. In this paper a novel modified Chi Square-based feature clustering and weighting scheme is proposed for the sentiment analysis of twitter message. Along with the part of speech tagging, the discriminability and dependency of the words in the tagged training dataset are taken into account in the clustering and weighting process. The multinomial Naïve Bayes model is also employed to handle redundant features, and the influence of emotional words is raised for maximizing the accuracy. Computer simulation with Sentiment 140 workload shows that the proposed scheme significantly outperforms four existing representative sentiment analysis schemes in terms of the accuracy regardless of the size of training and test data.

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