在线健康社区慢性病患者评论主题情感协同挖掘研究——以甜蜜家园为例  

Examining Topics and Sentiments of Chronic Disease Patients’Online Reviews—Case Study of“Sweet Homeland”

作　　者：余佳琪 赵豆豆 刘蕤[1] Yu Jiaqi;Zhao Doudou;Liu Rui(School of Information Management,Central China Normal University,Wuhan 430079,China;Hubei Normal University Library,Huangshi 435002,China)

机构地区：[1]华中师范大学信息管理学院,武汉430079 [2]湖北师范大学图书馆,黄石435002

出　　处：《数据分析与知识发现》2023年第10期95-108,共14页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金重大项目(项目编号:22&ZD324)的研究成果之一。

摘　　要：【目的】为及时掌握慢性病患者在不同患病阶段的关注主题与情感类型,构建评论主题情感协同挖掘模型。【方法】在LDA主题模型的基础上融入情感信息与时间信息,提出动态主题情感混合模型dUTSU。基于糖尿病患者评论数据,从主题识别效果、情感分类准确率验证模型有效性,开展主题-情感词分析和主题情感演化分析实现糖尿病患者不同患病阶段评论主题与情感协同挖掘。【结果】dUTSU的困惑度、主题平均相似度、情感分类准确率均优于JST、ASUM、UTSU等同类模型;利用dUTSU分析糖尿病患者评论数据,共识别出15个主题,得到疾病确诊阶段、并发症阶段等共7个时间片内的热点主题与伴生的情感强度及类型,揭示了主题情感随时间演化的特征。【局限】采用糖尿病患者评论数据开展实验,研究场景较为单一;在建模时仅考虑了时间属性,没有考虑患者的地理位置、个人属性、社交关系等因素对主题与情感的影响。【结论】dUTSU模型能够有效实现患者不同患病阶段的评论主题与情感协同挖掘,分析结果可为在线健康社区、医疗机构及患者自身进行健康服务与干预提供依据。[Objective]This paper constructs a model for topic-sentiment collaborative mining,aiming to understand chronic disease patients at different stages better.[Methods]First,we added sentiment and time features to the LDA model to create the new dUTSU(dynamic unsupervised topic and sentiment unification)model.Then,we retrieved posts by diabetes patients from an online health community.Finally,we assessed the dUTSU model’s performance with the topic-sentiment analysis and the topic-sentiment evolution analysis.[Results]The dUTSU model had better perplexity,average topic similarity,and sentiment classification accuracy than the JST,ASUM,and UTSU models.The model identified 15 topics and captured trending topics,sentiment,and intensity across seven distinct periods,including the disease diagnosis stage and the complication stage.The model also revealed the topic-sentiment evolution over time.[Limitations]The experiment only used the diabetics reviews.We did not consider patients’geographical locations,personal attributes,and social relationships.[Conclusions]The dUTSU model could effectively extract topic-sentiment data collaboratively reviews from patients with chronic diseases.The findings can serve as valuable references for online health communities,medical institutions,and patients to carry out health services.

关 键 词：在线健康社区 主题情感混合模型 演化分析 慢性病 

分 类 号：G350[文化科学—情报学] R197[医药卫生—卫生事业管理] TP391[医药卫生—公共卫生与预防医学]