Tsubasa Nakagawa
1st-year master's student at Iyatomi Lab.
Major in Applied Informatics, Graduate School of Science and Engineering, Hosei University
2022-10-25
この度、アトランタで開催されたCIKM 2022 に参加し、short paper として採択された論文のポスター発表を行いました。 今回採択された論文はこちらです。 Tsubasa Nakagawa, Shunsuke Kitada, Hitoshi Iyatomi [Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents](https://doi.org/10.1145/3511808.3557599) Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22) 内容としては書き手と読み手の感情認識の差の原因となる表現を検出するフレームワークの提案となっています。 今回が初めての国際会議であり、初めての海外だったため、毎日が新鮮でした。 未熟な自分がこうして無事発表を終えれたのも、論文執筆の段階から全面的にサポートしていただいた教授と先輩のおかげです。 つくづく周りの環境に恵まれているなと実感させられます。 最高に充実した10日間でした！ 今回発表に使用したポスターは[こちら](/posts/attended-cikm-2022/poster.pdf) ## 発表の様子 
2022-08-07
この度、主著論文が CIKM 2022 に short paper として採択されました。 内容としては書き手と読み手の感情認識の差の原因となる表現を検出するフレームワークの提案となっています。 Tsubasa Nakagawa, Shunsuke Kitada, Hitoshi Iyatomi [Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents](https://doi.org/10.1145/3511808.3557599) Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM '22) ## Abstract 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.
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