A Survey on Sentiment Computing for the Opinions Based on the Twitter

Abstract

The sentiment computing of opinions is a significant component of the social media big data. The explosive increase of the social media data on the web has created and promoted the development of the social media big data mining area. It has attracted many researches, which could support many realworld applications, such as public opinion monitoring for governments and recommendation for websites. However, existing sentiment computing methods are mainly based on the standard emotion thesaurus or supervised methods, which are not scalable to the social media big data. Therefore, we propose an innovative method to do the sentiment computing for opinions. More specially, based on the social media data (i.e., words and emoticons) of a Tweets, a Word Emotion Association Network (WEAN) is built to jointly express its semantics and emotions, which lays the foundation for the opinion sentiment computation. Based on WEAN, a word emotion computation algorithm is proposed to obtain the initial word emotions which are further refined through the standard emotion thesaurus. With the word emotions in hand, we can compute every sentence’s sentiment. Experimental results on real-world datasets demonstrate the excellent performance of the proposed method on the emotion computing for opinions.