Clue Extraction for Fine-Grained Emotion Analysis

Abstract

The emotion analysis in texts is a popular task in natural language processing. Existing research mainly recognizes the types of emotions by encoding sentences. However, in many cases, pure explicit encoding is easy to lose the fine-grained emotional clues hidden in the sentences due to the complexity and subtlety of emotions. We argue that narratives are inextricably emotionally structured and narrative analysis is used as a method to study and examine emotional clues. In this paper, we propose a new unified task: clue extraction for fine-grained emotion analysis (CLUE), which attempts to extract fine-grained emotional clue triples (Why, How, What): Why emotions occur, How people express emotions and What emotions they trigger. We propose a span-based method to address this CLUE task, which directly takes all possible spans as input. The advantage of span is to ensure that each clue is not segmented and semantically complete. The experimental results on a benchmark emotional cause corpus prove the feasibility of the CLUE task as well as the effectiveness of our method.