Learning the mapping rules for sentiment analysis

There is an increasing popularity of people posting their feel-ings on microblogging such as Twitter. Sentiment analysis on the tweets allows organizations to monitor public' feelings towards a product or brand. In this paper, we model sentiment analysis problem as a multi-classcation approach that utilizes various feature types, including predicate-argument relation, hashtag, mention and emotion in the tweets. We describe a Content-Structure Correspondence (CSC) model that is able to represent diverse feature types in a tweet. We present a conceptual hierarchy to express the characteristics of a tweet. A multi-classsifcation approach is used to map tweet content to the conceptual hierarchy. The mapping patterns are learned to identify the sentiment of a tweet.