Summary of Multi-modal Sentiment Analysis Technology
Sentiment analysis refers to the use of computers to automatically analyze and determine the emotions that people want to express. It can play a significant role in human-computer interaction and criminal investigation and solving cases. The advancement of deep learning and traditional feature extra...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2021-06-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2787.shtml |
| _version_ | 1818923965411753984 |
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| author | LIU Jiming, ZHANG Peixiang, LIU Ying, ZHANG Weidong, FANG Jie |
| author_facet | LIU Jiming, ZHANG Peixiang, LIU Ying, ZHANG Weidong, FANG Jie |
| author_sort | LIU Jiming, ZHANG Peixiang, LIU Ying, ZHANG Weidong, FANG Jie |
| collection | DOAJ |
| description | Sentiment analysis refers to the use of computers to automatically analyze and determine the emotions that people want to express. It can play a significant role in human-computer interaction and criminal investigation and solving cases. The advancement of deep learning and traditional feature extraction algorithms provides conditions for the use of multiple modalities for sentiment analysis. Combining multiple modalities for sentiment analysis can make up for the instability and limitations of single-modal sentiment analysis, and can effectively improve accuracy. In recent years, researchers have used three modalities of facial expression information, text information, and voice information to perform sentiment analysis. This paper mainly summarizes the multi-modal sentiment analysis technology from these three modalities. Firstly, it briefly introduces the basic concepts and research status of multi-modal sentiment analysis. Secondly, it summarizes the commonly used multi-modal sentiment analysis datasets. It gives a brief description of the existing single-modal emotion analysis technology based on facial expression information, text information and voice information. Next, the modal fusion technology is introduced in detail, and the existing results of the multi-modal sentiment analysis technology are mainly described according to different modal fusion methods. Finally, it discusses the problems of multi-modal sentiment analysis and future development direction. |
| first_indexed | 2024-12-20T02:17:49Z |
| format | Article |
| id | doaj.art-c9811cadcf3643159df052882a850203 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-20T02:17:49Z |
| publishDate | 2021-06-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-c9811cadcf3643159df052882a8502032022-12-21T19:56:54ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182021-06-011561165118210.3778/j.issn.1673-9418.2012075Summary of Multi-modal Sentiment Analysis TechnologyLIU Jiming, ZHANG Peixiang, LIU Ying, ZHANG Weidong, FANG Jie01. School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 2. Center for Image and Information Processing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China 3. International Joint Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province, Xi'an 710121, China 4. Key Laboratory of Electronic Information Application Technology for Crime Scene Investigation, Ministry of Public Security, Xi'an University of Posts and Telecommunications, Xi'an 710121, ChinaSentiment analysis refers to the use of computers to automatically analyze and determine the emotions that people want to express. It can play a significant role in human-computer interaction and criminal investigation and solving cases. The advancement of deep learning and traditional feature extraction algorithms provides conditions for the use of multiple modalities for sentiment analysis. Combining multiple modalities for sentiment analysis can make up for the instability and limitations of single-modal sentiment analysis, and can effectively improve accuracy. In recent years, researchers have used three modalities of facial expression information, text information, and voice information to perform sentiment analysis. This paper mainly summarizes the multi-modal sentiment analysis technology from these three modalities. Firstly, it briefly introduces the basic concepts and research status of multi-modal sentiment analysis. Secondly, it summarizes the commonly used multi-modal sentiment analysis datasets. It gives a brief description of the existing single-modal emotion analysis technology based on facial expression information, text information and voice information. Next, the modal fusion technology is introduced in detail, and the existing results of the multi-modal sentiment analysis technology are mainly described according to different modal fusion methods. Finally, it discusses the problems of multi-modal sentiment analysis and future development direction.http://fcst.ceaj.org/CN/abstract/abstract2787.shtmlmulti-modalsentiment analysismodal fusion |
| spellingShingle | LIU Jiming, ZHANG Peixiang, LIU Ying, ZHANG Weidong, FANG Jie Summary of Multi-modal Sentiment Analysis Technology Jisuanji kexue yu tansuo multi-modal sentiment analysis modal fusion |
| title | Summary of Multi-modal Sentiment Analysis Technology |
| title_full | Summary of Multi-modal Sentiment Analysis Technology |
| title_fullStr | Summary of Multi-modal Sentiment Analysis Technology |
| title_full_unstemmed | Summary of Multi-modal Sentiment Analysis Technology |
| title_short | Summary of Multi-modal Sentiment Analysis Technology |
| title_sort | summary of multi modal sentiment analysis technology |
| topic | multi-modal sentiment analysis modal fusion |
| url | http://fcst.ceaj.org/CN/abstract/abstract2787.shtml |
| work_keys_str_mv | AT liujimingzhangpeixiangliuyingzhangweidongfangjie summaryofmultimodalsentimentanalysistechnology |