1School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China 2Hubei Provincial Research Center for Health Technology Assessment, Wuhan 430030, China
[Objective] This paper tries to enrich the event logic of traditional fine-grained sentiment analysis from the perspective of emotion-triggering events. [Methods] We analyzed the OCC model’s sentiment generation rules and conditions to create the <event, sentiment> binary groups using event extraction and text classification methods. [Results] The proposed model constructed rules for emotion generation and built a theoretical basis for classifying sentiments. The model effectively identified emotion-triggering events (F1=0.933 8) and sentiments (F1=0.963 7). It generated <event, sentiment> binary groups (F1=0.889 2) to realize event-level fine-grained sentiment analysis. [Limitations] The structure of sentiment generation rules is simple and cannot reflect the diversity of netizens’ emotions. The corpus built at present has domain limitations and each corpus only contains one type of emotion-triggering event. [Conclusions] By associating event evaluations and emotions with the help of the OCC model, our new model becomes more in line with human thinking. The model has good interpretability and transferability, which enhances the granularity level of emotional objects in existing studies. It provides new ideas for research in the field of textual sentiment analysis.
沈丽宁, 杨佳艺, 裴家旋, 曹广, 陈功正. 基于OCC模型和情绪诱因事件抽取的细颗粒度情绪识别方法研究*[J]. 数据分析与知识发现, 2023, 7(2): 72-85.
Shen Lining, Yang Jiayi, Pei Jiaxuan, Cao Guang, Chen Gongzheng. A Fine-Grained Sentiment Recognition Method Based on OCC Model and Triggering Events. Data Analysis and Knowledge Discovery, 2023, 7(2): 72-85.
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