Evaluations on sentiment analysis of micro blogging site using topic modeling

Sentiment analysis of microblogging sites like Twitter has attracted much attention due to the rapid growth in Twitter's popularity as a platform for people to express their opinions and attitudes towards a great variety of topics. It has provided an economical and effective way to expose timely public sentiment, which is critical for decision making in various domains. This research work uses a Latent Dirichlet Allocation (LDA) based model, to filter foreground topics that can give potential interpretations of the sentiment variations and separate out longstanding background topics. To further intensify the readability of the mined reasons generative model based on LDA model is proposed to rank tweets respect to their “popularity” within the variation period. Models we have presented are general; they can be also used to discover special topics or aspects in one text collection in comparison with another background text collection.