Improving emotion classification on Chinese microblog texts with auxiliary cross-domain data

Abstract

Emotion classification for microblog texts has wide applications such as in social security and business marketing areas. The amount of annotated microblog texts is very limited. In this paper, we therefore study how to utilize annotated data from other domains (source domain) to improve emotion classification on microblog texts (target domain). Transfer learning has been a successful approach for cross domain learning. However, to the best of our knowledge, little attention has been paid for automatically selecting the appropriate samples from the source domain before applying transfer learning. In this paper, we propose an effective framework to sampling available data in the source domain before transfer learning, which we name as Two-Stage Sampling. The improvement of emotion classification on Chinese microblog texts demonstrates the effectiveness of our approach.