Explore the Effects of Emoticons on Twitter Sentiment Analysis

摘要

In recent years, Twitter Sentiment Analysis (TSA) has become a hot research topic. The target of this task is to analyse the sentiment polarity of the tweets. There are a lot of machine learning methods specifically developed to solve TSA problems, such as fully supervised method, distantly supervised method and combined method of these two. Considering the specialty of tweets that a limitation of 140 characters, emoticons have important effects on TSA. In this paper, we compare three emoticon pre-processing methods: emotion deletion (emoDel), emoticons 2-valued translation (emo2label) and emoticon explanation (emo2explanation). Then, we propose a method based on emoticon-weight lexicon, and conduct experiments based on Naive Bayes classifier, to validate the crucial role emoticons play on guiding emotion tendency in a tweet. Experiments on real data sets demonstrate that emoticons are vital to TSA.