{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:32:20Z","timestamp":1780637540840,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Recently, emotion detection in conversations becomes a hot research topic in the Natural Language Processing community. In this paper, we focus on emotion detection in multi-speaker conversations instead of traditional two-speaker conversations in existing studies. Different from non-conversation text, emotion detection in conversation text has one specific challenge in modeling the context-sensitive dependence. Besides, emotion detection in multi-speaker conversations endorses another specific challenge in modeling the speaker-sensitive dependence. To address above two challenges, we propose a conversational graph-based convolutional neural network. On the one hand, our approach represents each utterance and each speaker as a node. On the other hand, the context-sensitive dependence is represented by an undirected edge between two utterances nodes from the same conversation and the speaker-sensitive dependence is represented by an undirected edge between an utterance node and its speaker node. In this way, the entire conversational corpus can be symbolized as a large heterogeneous graph and the emotion detection task can be recast as a classification problem of the utterance nodes in the graph. The experimental results on a multi-modal and multi-speaker conversation corpus demonstrate the great effectiveness of the proposed approach.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/752","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"5415-5421","source":"Crossref","is-referenced-by-count":140,"title":["Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations"],"prefix":"10.24963","author":[{"given":"Dong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangqing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changlong","family":"Sun","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoushan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaoming","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:51:34Z","timestamp":1564300294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/752"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/752","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}