Emotion classification of chinese microblog text via fusion of BoW and eVector feature representations

Abstract

Sentiment Analysis has been a hot research topic in recent years. Emotion classification is more detailed sentiment analysis which cares about more than the polarity of sentiment. In this paper, we present our system of emotion analysis for the Sina Weibo texts on both the document and sentence level, which detects whether a text is sentimental and further decides which emotion classes it conveys. The emotions of focus are seven basic emotion classes: anger, disgust, fear, happiness, like, sadness and surprise. Our baseline system uses supervised machine learning classifier (support vector machine, SVM) based on bag-of-words (BoW) features. In a contrast system, we propose a novel approach to construct an emotion lexicon and to generate a new feature representation of text which is named emotion vector eVector. Our experimental results show that both systems can classify emotion significantly better than random guess. Fusion of both systems obtains additional gain which indicates that they capture certain complementary information.