论文标题
通过自然语言文本的深度学习模型进行情感相关性挖掘
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text
论文作者
论文摘要
情绪分析吸引了研究人员的注意。人工智能领域的大多数作品都集中在识别情感上，而不是挖掘未被错误认可的情感的原因。情绪之间的相关性有助于情绪识别的失败。在本文中，我们试图通过Web News的自然语言文本来填补情感识别与情感相关性挖掘之间的空白。情感之间的相关性，表达为情感的混乱和演变，主要是由人类情感认知偏见引起的。为了挖掘情绪相关性，从情感识别到文本，提出了三种特征和两种深度神经网络模型。情绪混乱法是通过正交基础提取的。情绪进化定律是从三个角度评估的，一步转移，有限的步骤和最短的路径转移。该方法使用三个数据集验证 - 标题，身体和新闻文章的评论，涵盖了不同长度（长和短）的客观和主观文本。实验结果表明，在主观评论中，情绪很容易被误认为是愤怒。评论往往会引起情绪流通和悲伤的愤怒。在客观新闻中，很容易将文本情感视为爱情并引起恐惧乔伊的流通。这意味着，记者可能会试图用恐惧和喜悦的话引起人们的注意，但会引起情感之爱。新闻发布后，网民产生情感评论来表达他们的激烈情绪，即愤怒，悲伤和爱。这些发现可以为有关情感互动的应用提供见解，例如网络公共情感，社交媒体传播和人类计算机的互动。
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.
