Emotion inference of text based on counterfactual behavior knowledge

Abstract

Reader’s emotion is triggered by writer’s expression and content of text. Reader’s emotion inference can help industries and companies discover the preferences and needs of readers and customize appealing content. Currently, most scholars focus on mining emotion expressed by writers, while neglecting easily-transferred reader’s emotion which is simultaneously influenced by objective event content, subjective writer’s affect and individual cognition. Faced with that, we propose a reader’s emotion inference method based on counterfactual behavior knowledge. Multi-granularity elements are extracted, including event indicator, behavior driver and subjective words that hidden in text. Three steps are included in this method. First, counterfactual behavior knowledge is constructed by replacing A–F–B knowledge in original text, which assists models to process and understand the relationships between events and emotions according to the fusion of facts and counterfacts. Second, a knowledge prompt method is proposed, which splice the A–F–B knowledge (as a new feature) to supplement and reinforce established facts. Third, a decision enhancement method is proposed, which employs self-reflection mechanism to fuse original decision and improved decision based on emotion transfer preferences. Experiments and a questionnaire survey are conducted on social news data with reader’s emotion votes. The results show that the models with proposed method outperforms baseline models.