Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents
复制标题
DOI：
10.1145/3511808.3557599
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Tsubasa Nakagawa;Shunsuke Kitada;H. Iyatomi
中科院分区：
文献类型：
--
作者：
Tsubasa Nakagawa;Shunsuke Kitada;H. Iyatomi
通常很难从在线交换的文本中正确推断作者的情感，并且作者和读者之间的认知差异可能会出现问题。在本文中，我们提出了一个新的框架，用于检测在作者和读者之间产生情感识别差异的句子，以及检测导致这种差异的表达类型。所提出的框架由基于 Transformer (BERT) 的检测器的双向编码器表示组成，该检测器检测导致情感识别差异的句子，并进行分析，获取此类句子中典型出现的表达。该检测器基于日本 SNS 文档数据集，其中包含由社交网络服务 (SNS) 文档的作者和三位读者注释的情感标签，检测到 AUC = 0.772 的“隐藏愤怒句子”；这些句子引起了对愤怒的认识的差异。由于SNS文档中包含许多句子，其含义极难解释，通过分析该检测器检测到的句子，我们获得了几种在隐怒句子中典型出现的表达方式。检测到的句子和表情没有明确表达愤怒，很难推断出作者的愤怒，但如果指出隐含的愤怒，就可以猜测作者为什么生气。投入实际使用后，该框架可能能够缓解基于误解的问题。
It is often difficult to correctly infer a writer's emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create differences in emotion recognition between the writer and the reader and for detecting the kinds of expressions that cause such differences. The proposed framework consists of a bidirectional encoder representations from transformers (BERT)-based detector that detects sentences causing differences in emotion recognition and an analysis that acquires expressions that characteristically appear in such sentences. The detector, based on a Japanese SNS-document dataset with emotion labels annotated by both the writer and three readers of the social networking service (SNS) documents, detected "hidden-anger sentences" with AUC = 0.772; these sentences gave rise to differences in the recognition of anger. Because SNS documents contain many sentences whose meaning is extremely difficult to interpret, by analyzing the sentences detected by this detector, we obtained several expressions that appear characteristically in hidden-anger sentences. The detected sentences and expressions do not convey anger explicitly, and it is difficult to infer the writer's anger, but if the implicit anger is pointed out, it becomes possible to guess why the writer is angry. Put into practical use, this framework would likely have the ability to mitigate problems based on misunderstandings.