  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.
