Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion Methods
As a branch of sentiment analysis tasks, emotion recognition in conversation (ERC) aims to explore the hidden emotions of a speaker by analyzing the sentiments in utterance. In addition, emotion recognition in multimodal data from conversation includes the text of the utterance and its corresponding...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/22/4714 |
| _version_ | 1797459480303108096 |
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| author | Yao Fu Shaoyang Yuan Chi Zhang Juan Cao |
| author_facet | Yao Fu Shaoyang Yuan Chi Zhang Juan Cao |
| author_sort | Yao Fu |
| collection | DOAJ |
| description | As a branch of sentiment analysis tasks, emotion recognition in conversation (ERC) aims to explore the hidden emotions of a speaker by analyzing the sentiments in utterance. In addition, emotion recognition in multimodal data from conversation includes the text of the utterance and its corresponding acoustic and visual data. By integrating features from various modalities, the emotion of utterance can be more accurately predicted. ERC research faces challenges in context construction, speaker dependency design, and multimodal heterogeneous feature fusion. Therefore, this review starts by defining the ERC task, developing the research work, and introducing the utilized datasets in detail. Simultaneously, we analyzed context modeling in conversations, speaker dependency, and methods for fusing multimodal information concerning existing research work for evaluation purposes. Finally, this review also explores the research, application challenges, and opportunities of ERC. |
| first_indexed | 2024-03-09T16:51:58Z |
| format | Article |
| id | doaj.art-a102ea63768d4c6c8d633247b9d9118d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T16:51:58Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a102ea63768d4c6c8d633247b9d9118d2023-11-24T14:39:48ZengMDPI AGElectronics2079-92922023-11-011222471410.3390/electronics12224714Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion MethodsYao Fu0Shaoyang Yuan1Chi Zhang2Juan Cao3State Key Laboratory of Media Convergence and Communication, Communication University of China, Dingfuzhuang, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Dingfuzhuang, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Dingfuzhuang, Beijing 100024, ChinaState Key Laboratory of Media Convergence and Communication, Communication University of China, Dingfuzhuang, Beijing 100024, ChinaAs a branch of sentiment analysis tasks, emotion recognition in conversation (ERC) aims to explore the hidden emotions of a speaker by analyzing the sentiments in utterance. In addition, emotion recognition in multimodal data from conversation includes the text of the utterance and its corresponding acoustic and visual data. By integrating features from various modalities, the emotion of utterance can be more accurately predicted. ERC research faces challenges in context construction, speaker dependency design, and multimodal heterogeneous feature fusion. Therefore, this review starts by defining the ERC task, developing the research work, and introducing the utilized datasets in detail. Simultaneously, we analyzed context modeling in conversations, speaker dependency, and methods for fusing multimodal information concerning existing research work for evaluation purposes. Finally, this review also explores the research, application challenges, and opportunities of ERC.https://www.mdpi.com/2079-9292/12/22/4714emotion recognition in conversationspeaker dependencycontext constructfusion methodfeature extractionmultimodal data |
| spellingShingle | Yao Fu Shaoyang Yuan Chi Zhang Juan Cao Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion Methods Electronics emotion recognition in conversation speaker dependency context construct fusion method feature extraction multimodal data |
| title | Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion Methods |
| title_full | Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion Methods |
| title_fullStr | Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion Methods |
| title_full_unstemmed | Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion Methods |
| title_short | Emotion Recognition in Conversations: A Survey Focusing on Context, Speaker Dependencies, and Fusion Methods |
| title_sort | emotion recognition in conversations a survey focusing on context speaker dependencies and fusion methods |
| topic | emotion recognition in conversation speaker dependency context construct fusion method feature extraction multimodal data |
| url | https://www.mdpi.com/2079-9292/12/22/4714 |
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