Explore the Effects of Emoticons on Twitter Sentiment Analysis
Katarzyna M. Węgrzyn-Wolska, Lamine Bougueroua, Haichao Yu, Jing Zhong · 2016
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.