Twitter Sentiment Analysis -- A More Enhanced Way of Classification and Scoring

In this paper we present a novel approach to Twitter Sentiment Analysis. The approach adopted is to analyse the lexicon features of the tweets for classifying its sentiment (positive, negative and neutral). The training data is made more exhaustive by including various manually labelled tweets, in addition to the existing word stock to keep up with the changing micro logging trends. For Data Preprocessing, a novel spell checking algorithm is introduced, an operation for disjoining compound words such as "high hopes" is implemented and emoticons are replaced by suitable emotion words like happy or sad. After this initial preprocessing, the machine learning algorithms are (Support vector machines and Maximum entropy) are applied. We also propose an avant-garde sentiment scoring mechanism to estimate the degree of the sentiment. Our approach is able to assign sentiments to tweets with an accuracy of 80%.