Learning Sentence Representation for Emotion Classification on Microblogs

This paper studies the emotion classification task on microblogs. Given a message, we classify its emotion as happy, sad, angry or surprise. Existing methods mostly use the bag-of-word representation or manually designed features to train supervised or distant supervision models. However, manufacturing feature engines is time-consuming and not enough to capture the complex linguistic phenomena on microblogs. In this study, to overcome the above problems, we utilize pseudo-labeled data, which is extensively explored for distant supervision learning and training language model in Twitter sentiment analysis, to learn the sentence representation through Deep Belief Network algorithm. Experimental results in the supervised learning framework show that using the pseudolabeled data, the representation learned by Deep Belief Network outperforms the Principal Components Analysis based and Latent Dirichlet Allocation based representations. By incorporating the Deep Belief Network based representation into basic features, the performance is further improved.