Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
文献类型：期刊论文
| 作者 | Sun, Yawei1,2; He, Saike3 ; Han, Xu4; Luo, Yan5
|
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 14期号:7页码:23 |
| 关键词 | sentiment cue extraction self-supervised learning interpretable machine learning |
| DOI | 10.3390/app14072737 |
| 通讯作者 | He, Saike(saike.he@ia.ac.cn) |
| 英文摘要 | In this paper, we present a novel self-supervised framework for Sentiment Cue Extraction (SCE) aimed at enhancing the interpretability of text sentiment analysis models. Our approach leverages self-supervised learning to identify and highlight key textual elements that significantly influence sentiment classification decisions. Central to our framework is the development of an innovative Mask Sequence Interpretation Score (MSIS), a bespoke metric designed to assess the relevance and coherence of identified sentiment cues within binary text classification tasks. By employing Monte Carlo Sampling techniques optimized for computational efficiency, our framework demonstrates exceptional effectiveness in processing large-scale text data across diverse datasets, including English and Chinese, thus proving its versatility and scalability. The effectiveness of our approach is validated through extensive experiments on several benchmark datasets, including SST-2, IMDb, Yelp, and ChnSentiCorp. The results indicate a substantial improvement in the interpretability of the sentiment analysis models without compromising their predictive accuracy. Furthermore, our method stands out for its global interpretability, offering an efficient solution for analyzing new data compared to traditional techniques focused on local explanations. |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001200808300001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/58095]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | He, Saike |
| 作者单位 | 1.Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Trustworthy Distributed Comp & Serv BUPT, Beijing 100876, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 4.Inst Sci & Tech Informat China, Beijing 100038, Peoples R China 5.Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Informat, Beijing 100020, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Yawei,He, Saike,Han, Xu,et al. Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction[J]. APPLIED SCIENCES-BASEL,2024,14(7):23. |
| APA | Sun, Yawei,He, Saike,Han, Xu,&Luo, Yan.(2024).Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction.APPLIED SCIENCES-BASEL,14(7),23. |
| MLA | Sun, Yawei,et al."Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction".APPLIED SCIENCES-BASEL 14.7(2024):23. |
入库方式： OAI收割
来源：自动化研究所
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