Institutional Repository of Computer Application Research Laboratory
| EMFSA: Emoji-based multifeature fusion sentiment analysis | |
| Tang, Hongmei1,2; Tang, Wenzhong1; Zhu, Dixiongxiao1; Wang, Shuai1; Wang, Yanyang3,4; Wang, Lihong5 | |
| 2024-09-19 | |
| Source Publication | PLOS ONE
 |
| ISSN | 1932-6203 |
| Volume | 19Issue:9Pages:e0310715 |
| Contribution Rank | 2 |
| Abstract | Short texts on social platforms often suffer from insufficient emotional semantic expressions, sparse features, and polysemy. To enhance the accuracy achieved by sentiment analysis for short texts, this paper proposes an emoji-based multifeature fusion sentiment analysis model (EMFSA). The model mines the sentiments of emojis, topics, and text features. Initially, a pretraining method for feature extraction is employed to enhance the semantic expressions of emotions in text by extracting contextual semantic information from emojis. Following this, a sentiment- and emoji-masked language model is designed to prioritize the masking of emojis and words with implicit sentiments, focusing on learning the emotional semantics contained in text. Additionally, we proposed a multifeature fusion method based on a cross-attention mechanism by determining the importance of each word in a text from a topic perspective. Next, this method is integrated with the original semantic information of emojis and the enhanced text features, attaining improved sentiment representation accuracy for short texts. Comparative experiments conducted with the state-of-the-art baseline methods on three public datasets demonstrate that the proposed model achieves accuracy improvements of 2.3%, 10.9%, and 2.7%, respectively, validating its effectiveness. |
| DOI | 10.1371/journal.pone.0310715 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[62272022] ; National Key Research and Development Program of China[210YBXM2024106007] ; National Key Research and Development Program of China[2022YFB3207700] |
| WOS Research Area | Science & Technology - Other Topics |
| WOS Subject | Multidisciplinary Sciences |
| WOS ID | WOS:001316557200076 |
| Publisher | PUBLIC LIBRARY SCIENCE |
| Funding Organization | National Natural Science Foundation of China ; National Key Research and Development Program of China |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/7022 |
| Collection | 计算机技术应用研究室 |
| Corresponding Author | Tang, Wenzhong; Wang, Shuai |
| Affiliation | 1.Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China 2.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi, Peoples R China 3.Beihang Univ, Sch Aeronaut Sci & Engn, Beijing, Peoples R China 4.Jiangxi Res Inst Beihang Univ, Nanchan, Peoples R China 5.Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing, Peoples R China |
| First Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Tang, Hongmei,Tang, Wenzhong,Zhu, Dixiongxiao,et al. EMFSA: Emoji-based multifeature fusion sentiment analysis[J]. PLOS ONE,2024,19(9):e0310715. |
| APA | Tang, Hongmei,Tang, Wenzhong,Zhu, Dixiongxiao,Wang, Shuai,Wang, Yanyang,&Wang, Lihong.(2024).EMFSA: Emoji-based multifeature fusion sentiment analysis.PLOS ONE,19(9),e0310715. |
| MLA | Tang, Hongmei,et al."EMFSA: Emoji-based multifeature fusion sentiment analysis".PLOS ONE 19.9(2024):e0310715. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Tang-2024-EMFSA_ Emo（1527KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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