Build Chinese Emotion Lexicons Using A Graph-based Algorithm and Multiple Resources

For sentiment analysis, lexicons play an important role in many related tasks. In this paper, aiming to build Chinese emotion lexicons for public use, we adopted a graph-based algorithm which ranks words according to a few seed emotion words. The ranking algorithm exploits the similarity between words, and uses multiple similarity metrics which can be derived from dictionaries, unlabeled corpora or heuristic rules. To evaluate the adopted algorithm and resources, two independent judges were asked to label the top words of ranking list. It is observed that noise is almost unavoidable due to imprecise similarity metrics between words. So, to guarantee the quality of emotion lexicons, we use an iterative feedback to combine manual labeling and the automatic ranking algorithm above. We also compared our newly constructed Chinese emotion lexicons (happiness, anger, sadness, fear and surprise) with existing counterparts, and related analysis is offered.