摘要
[目的/意义]在线健康社区为用户提供线上健康服务,分析其用户评论的潜在信息,对于医疗服务质量的提高和健康社区信息建设的优化具有重要意义。[方法/过程]本文提出了一个在线健康社区用户评论分析模型。首先,通过隐含狄利克雷分布(Latent Dirichlet Allocation,LDA)主题模型挖掘患者评论的主题;其次,使用分类模型对患者评论进行主题分类;最后,通过词频-逆文档频率(Term Frequency-Inverse Document Fre⁃quency,TF-IDF)方法以及情感倾向点互信息(Semantic Orientation Pointwise Mutual Information,SO-PMI)方法构建领域情感词典,计算各个主题的患者评论文本的情感得分,分析不同情感倾向的评论信息。[结果/结论]通过分析“好大夫在线”综合性三甲医院的用户评论数据,对其进行实证研究,根据实验结果的信息内容和规律,提出了改进医疗服务和信息建设的相关参考建议。
[Objective/Significance]Online health communities provide online health services for users,and analy⁃zing the potential information of user comments is of great significance for improving the quality of medical services and opti⁃mizing the information construction of healthy communities.[Method/Process]The study proposed an online health com⁃munity user comment analysis model.Firstly,the LDA topic model was used to dig the topic of patient comments.Next,the classification models were used to classify the patient comments into different topics.Finally,word frequency screen⁃ing,TF-IDF and SO-PMI method were used to construct a domain sentiment dictionary which was employed to calculate the sentiment score of patient comments in each topics.And comment information of different sentiments were further ana⁃lyzed.[Result/Conclusion]Empirical research is conducted by analyzing the user comment data of General Grade Three hospital from“Good Doctor Online”.According to the information content and rules of the experimental results,relevant reference suggestions for improving medical service and information construction are put forward.
作者
郭羽婷
姚宣合
Guo Yuting;Yao Xuanhe(Department of Information Science and Technology,Northeast Normal University,Changchun 130117,China)
出处
《现代情报》
北大核心
2025年第8期135-145,共11页
Journal of Modern Information
基金
国家自然科学基金项目“基于多目标优化的MOOCs课程视频摘要生成框架研究”(项目编号:62107009)
东北师范大学校内青年基金项目“基于深度特征表示和注意力机制的视频摘要框架研究”(项目编号:2109213)。
关键词
健康社区
用户评论
主题挖掘
文本分类
情感分析
healthy communities
user reviews
topic mining
text classification
sentiment analysis
