Related papers: Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

URL: http://arxiv.org/abs/2007.14071v1
Date: Tue, 28 Jul 2020 08:59:16 GMT
Title: Emotion Correlation Mining Through Deep Learning Models on Natural Language Text
Authors: Xinzhi Wang, Luyao Kou, Vijayan Sugumaran, Xiangfeng Luo, and Hui Zhang
Abstract summary: We try to fill the gap between emotion recognition and emotion correlation mining through natural language text from web news. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural network models are presented.
Score: 3.23176099204268
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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