Weighted Domain Translation for Online News Comments Emotion Tagging

This paper studies an emotion classification problem, which aims to classify online news comments to one of fine-grained emotion categories, e.g. happy, sad, and angry, etc. Neural networks have been widely used and achieved great success in sentiment classification. However, there must be sufficient labeled comments available for training neural networks, which usually requires labor-intensive and time-consuming manual labeling. One of the most effective solutions is to apply transfer learning, which uses abundant labeled comments from a source news domain to help the classification for another target domain with limited amount of labeled data. Still, the comments from different domains can have very different word distributions, which makes it difficult to transfer knowledge from one domain to another. In this paper, we accomplish cross-domain emotion tagging based on an advanced neural network BLSTM (bidirectional long short-term memory) with \"domain translation'', which can overcome the difference between domains. A weighted linear transformation is utilized to \"translate'' knowledge from source to target domain. An extensive set of experimental results on four datasets from popular online news services demonstrates the effectiveness of our proposed models.