基于OCC模型和贝叶斯网络的情绪句分类方法

情绪句分类是情绪分析研究领域的核心问题之一,旨在解决情绪句类别的自动判断问题。传统基于情绪认知模型(OCC模型)的情绪句分类方法大多依赖词典和规则,在文本信息缺失的情况下分类精度不高。文中提出基于OCC模型和贝叶斯网络的情绪句分类方法,通过分析OCC模型的情绪生成规则,提取情绪评估变量并结合情绪句中含有的表情符号特征构建情绪分类贝叶斯网络;通过概率推理,可以实现句子级文本的情绪分类,并减小句中信息缺失所带来的影响。与NLPCC2014中文微博情绪分析评测的子任务情绪句分类评测结果的对比表明,所提方法具有有效性。

Emotional Sentence Classification Method Based on OCC Model and Bayesian Network

Emotional sentence classification is one of the core problems in the field of emotional analysis.It aims to solve the problem of automatic judgment of emotional sentence categories.Traditional emotional sentence classification methods based on OCC sentiment recognition models mostly rely on dictionaries and rules.In the absence of textual information,the classification accuracy is relatively lower.This paper proposed an emotional sentence classification method based on OCC model and Bayesian network.By analyzing the emotion generation rules of OCC model,it extracts emotional assessment variables and combines the emotion features contained in the emotion sentence to construct a Bayesian network of emotion classification.Through probabilistic reasoning,it is possible to identify a variety of emotion categories that the text may want to express and reduce the impact of missing text information.Compared with the NLPCC2014 Chinese Weibo emotion analysis evaluation sub-task emotional sentence classification evaluation results,the results show that the proposed method is effective.