Sentiment Classification Using Sociolinguistic Clusters

Sociolinguistic studies suggest the similarity of language use among people with similar social state, and recent large-scale computational analyses of online text are providing various supports, for example, the effect of social class, geography, and political preference on the language use. We approach the tasks of TASS 2015 with sociolinguistic insights in order to capture the patterns in the expression of sentiment. Our approach expands the scope of analysis from the text itself to the authors: their social state and political preference. The tweets of authors with similar social state or political preference are grouped as a cluster, and classifiers are built separately for each cluster to learn the linguistic style of that particular cluster. The approach can be further improved by combining it with other language processing and machine learning techniques.