Tweet Sentiment Analyzer: Sentiment Score Estimation Method for Assessing the Value of Opinions in Tweets

Abstract

Social networking applications are prominent among the internet user communities. Many social media websites are used for sharing the information instantly. Twitter is one of the vibrant social networking websites for sharing small textual information within a short span of time. It is essential to identify the type of information shared on these websites. Sentiment analysis involves the process of analyzing the opinion content present in the text. Millions of tweets are posted in a day about various topics. Twitter sentiment analysis mainly involves the process of identifying the polarity oriented terms mentioned in the tweet. Most of the twitter sentiment analysis works have concentrated on the sentiment polarity identification. Based on the literature, it is observed that, researchers still need to contribute in the area of sentiment score calculation of a tweet. Hence, in this work, sentiment score calculation is carried out with sentiment corpus oriented approach for calculating the score effectively. In addition, the grammatical type of the word used in a tweet, the relationship between the words are properly identified. The tweet tagger, corpus based sentiment score assignment have been distinctively used when compared to other previous works. The experimental results show that the sentimental score based tweet identification resulted in top tweets among the large collection of tweets.