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Language-independent Bayesian sentiment mining of Twitter

This paper outlines a new language-independent model for sentiment analysis of short, social-network statuses. We demonstrate this on data from Twitter, modelling happy vs sad sentiment, and show that in some circumstances this outperforms similar Naive Bayes models by more than 10%. We also propose an extension to allow the modelling of different sentiment distributions in different geographic regions, while incorporating information from neighbouring regions. We outline the considerations when creating a system analysing Twitter data and present a scalable system of data acquisition and prediction that can monitor the sentiment of tweets in real time.