Mining Sentiments from Tweets

A sentiment scoring function which uses prior information to classify (binary classification) and weight various sentiment bearing words/phrases in tweets achieves classification accuracy of 87% on Stanford Dataset and 88% on Mejaj dataset.

Abstract

Twitter is a micro blogging website, where users can post messages in very short text called Tweets. Tweets contain user opinion and sentiment towards an object or person. This sentiment information is very useful in various aspects for business and governments. In this paper, we present a method which performs the task of tweet sentiment identification using a corpus of pre-annotated tweets. We present a sentiment scoring function which uses prior information to classify (binary classification) and weight various sentiment bearing words/phrases in tweets. Using this scoring function we achieve classification accuracy of 87 % on Stanford Dataset and 88 % on Mejaj dataset. Using supervised machine learning approach, we achieve classification accuracy of 88 % on Stanford dataset. 1