Unsupervised Sentiment Classification of Twitter Data using Emoticons

Abstract

Twitter is a powerful social media where people share their opinion on various topics. Sentiment Analysis on twitter data gives the classification of opinion on a topic as positive, negative or neutral. Twitter messages are written informally and tweets are short. Hence, the classification of tweets by only considering the text part of the message does not give accurate results. To improve the classification accuracy we use Emotion Tokens like Emoticons or Emojis. Emotion Tokens are independent of language, grammar or size of the tweet. Considering Emotion tokens while classifying tweets will improve the accuracy of classification. In this paper, we propose unsupervised Sentiment classification on Twitter Data using Emoticons to improve the performance of classification