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

A Fine-Grained Sentiment Recognition Method Based on OCC Model and Triggering Events

【目的】从情绪诱因事件角度丰富传统细颗粒度情绪分析中的事件逻辑。【方法】分析OCC模型中的情绪生成规则和条件,利用事件抽取和文本分类方法生成<事件,情绪>二元组。【结果】研究构建了情绪生成规则,情绪类别划分具有理论基础。模型能够有效识别情绪诱因事件(F1=0.9338)及情绪(F1=0.9637),生成<事件,情绪>二元组(F1=0.8892),实现事件级细颗粒度情绪分析。【局限】情绪生成规则结构简单,难以体现网民情绪的多样性。现阶段构建的语料集存在领域局限性,每条语料只包含一种类型情绪诱因事件。【结论】借助OCC模型将事件评价和情绪相关联,让情绪识别更接近人类思维方式。模型的理解性和迁移性较强,提升了现有研究中情绪对象的粒度层次,为文本情绪分析领域研究提供新思路。

[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 thebinary 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=0.9338)and sentiments(F1=0.9637).It generatedbinary groups(F1=0.8892)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.