A Sentence Sentiment Classification reflecting Formal and Informal Vocabulary Information

Social Network Services(SNS) such as Twitter, Facebook and Myspace have gained popularity worldwide. Especially, sentiment analysis of SNS users` sentence is very important since it is very useful in the opinion mining. In this paper, we propose a new sentiment classification method of sentences which contains formal and informal vocabulary such as emoticons, and newly coined words. Previous methods used only formal vocabulary to classify sentiments of sentences. However, these methods are not quite effective because internet users use sentences that contain informal vocabulary. In addition, we construct suggest to construct domain sentiment vocabulary because the same word may represent different sentiments in different domains. Feature vectors are extracted from the sentiment vocabulary information and classified by Support Vector Machine(SVM). Our proposed method shows good performance in classification accuracy.