Extracting Emotion Causes Using Learning to Rank Methods From an Information Retrieval Perspective

Emotion cause extraction is a challenging task for the fine-grained emotion analysis. Even though a few studies have addressed the task using clause-level classification methods, most of them have partly ignored emotion-level context information. To comprehensively leverage the information, we propose a novel method based on learning to rank to identify emotion causes from an information retrieval perspective. Our method seeks to rank candidate clauses with respect to certain provoked emotions in analogy with query-level document ranking in information retrieval. To learn effective clause ranking models, we represent candidate clauses as feature vectors involving both emotion-independent features and emotion-dependent features. Emotion-independent features are extracted to capture the possibility that a clause is expected to provoke an emotion, and emotion-dependent features are extracted to capture the relevance between candidate cause clauses and their corresponding emotions. We investigate three approaches to learning to rank for emotion cause extraction in our method. We evaluate the performance of our method on an existing dataset for emotion cause extraction. The experimental results show that our method is effective in emotion cause extraction, significantly outperforming the state-of-the-art baseline methods in terms of the precision, recall, and F-measure.