Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

TL;DR

This paper explores how emotions in natural language text are correlated and how understanding these correlations can improve emotion recognition, revealing patterns in news and comments that impact affective computing applications.

Contribution

It introduces a novel approach combining features and deep neural networks to mine emotion correlations from web news texts, addressing the gap between emotion recognition and emotion correlation analysis.

Findings

In comments, emotions often mistaken as anger and tend to circulate between love and anger.

In objective news, emotions recognized as love, with fear-joy circulation observed.

Emotion patterns differ between subjective comments and objective news texts.

Abstract

Emotion analysis has been attracting researchers' attention. Most previous works in the artificial intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. Correlation among emotions contributes to the failure of emotion recognition. In this paper, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from web news. Correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural network models are presented. The emotion confusion law is extracted through orthogonal basis. The emotion evolution law is evaluated from three perspectives, one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets-the titles, the bodies, and the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that, in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love-anger and sadness-anger. In objective news, it is easy to recognize text emotion as love and cause fear-joy circulation. That means, journalists may try to attract attention using fear and joy words but arouse the emotion love instead; After news release, netizens generate emotional comments to express their intense emotions, i.e., anger, sadness, and love. These findings could provide insights for applications regarding affective interaction such as network public sentiment, social media communication, and human-computer interaction.