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数据分析与知识发现  2023, Vol. 7 Issue (10): 95-108     https://doi.org/10.11925/infotech.2096-3467.2022.0891
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
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*
余佳琪1,2,赵豆豆1,刘蕤1()
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
Examining Topics and Sentiments of Chronic Disease Patients’ Online Reviews — Case Study of “Sweet Homeland”
Yu Jiaqi1,2,Zhao Doudou1,Liu Rui1()
1School of Information Management, Central China Normal University, Wuhan 430079, China
2Hubei Normal University Library, Huangshi 435002, China
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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。

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余佳琪
赵豆豆
刘蕤
关键词 在线健康社区主题情感混合模型演化分析慢性病    
Abstract

[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.

Key wordsOnline Health Community    Joint Topic-Sentiment Model    Evolution Analysis    Chronic Disease
收稿日期: 2022-08-24      出版日期: 2023-03-28
ZTFLH:  G350  
  R197  
  TP391  
基金资助:*国家社会科学基金重大项目(22&ZD324)
通讯作者: 刘蕤,ORCID:0000-0002-5450-4947,E-mail:liuruiccnu@hotmail.com。   
引用本文:   
余佳琪, 赵豆豆, 刘蕤. 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*[J]. 数据分析与知识发现, 2023, 7(10): 95-108.
Yu Jiaqi, Zhao Doudou, Liu Rui. Examining Topics and Sentiments of Chronic Disease Patients’ Online Reviews — Case Study of “Sweet Homeland”. Data Analysis and Knowledge Discovery, 2023, 7(10): 95-108.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0891      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I10/95
Fig.1  dUTSU模型
Fig.2  不同主题下的困惑度指标结果
Fig.3  分类准确率
主题标签 部分主题词 主题类目
1 糖尿病、引发、病因、问题、体质、原因、情绪、习惯、熬夜、进食 病因咨询
2 糖尿病、血糖、患者、知识、控糖、空腹、胰岛素、指南、必备、分享 知识共享
3 血糖、控制、运动、降糖、健康、跑步、坚持、减肥、加油、锻炼 血糖控制
4 危害、身体、并发症、影响、视力、病变、健康、低血糖、波动、肾病 疾病危害
5 手术、治疗、需要、切除、胃流转、愈合、减重、患者、后遗症、恢复 手术咨询
6 吃药、胰岛素、减量、二甲双胍、剂量、副作用、稳定、价格、区别、损害 药物服用
7 检查、指标、糖化、结果、分泌、报告、糖耐量、异常、生化、血红蛋白 疾病检查
8 医院、糖尿病、看病、医生、专家、治疗、预约、医保、检查、报销 就医指导
9 治疗、医生、用药、方案、药物、患者、临床、重要、研究、有效 治疗方案及研究
10 糖尿病、前期、多久、并发症、发展、确诊、时间、病情、最终、延缓 疾病发展阶段
11 糖尿病、并发、预防、高血压、检查、发现、提前、低血糖、缓解、早期 疾病预防
12 早餐、吃饭、主食、空腹、米饭、血糖、脂肪、晚饭、牛奶、加餐 饮食
13 神经病变、并发症、症状、视力、典型、模糊、糖尿病足、疼痛、导致、 疾病症状咨询
14 血糖仪、试纸、馒头、强生、对比、血糖值、标准、指数、达标、不错 血糖仪产品
15 糖尿病、先天、遗传、孩子、基因、家族、发病、亲属、怀孕、几率 疾病遗传
Table 1  主题生成结果
Fig.4  主题-情感概率分布
积极情感 消极情感
主题3 主题9 主题14 主题6 主题7 主题13
血糖 治疗 血糖仪 吃药 检查 神经病变
控制 医生 试纸 胰岛素 指标 并发症
运动 用药 馒头 减量 糖化 症状
降糖 方案 强生 二甲双胍 结果 视力
健康 药物 对比 剂量 分泌 典型
跑步 患者 血糖值 副作用 报告 模糊
坚持 临床 标准 稳定 糖耐量 糖尿病足
减肥 重要 指数 价格 异常 疼痛
加油 研究 达标 区别 生化 导致
锻炼 有效 不错 损害 血红蛋白 引起
Table 2  <主题,情感>对下的词汇
Fig.5  积极情感强度演化
Fig.6  主题3高频关键词演化分析
Fig.7  消极情感强度演化
Fig.8  主题13高频关键词演化分析
主题

时间片
1 2 3 4 5 6 7
(+) 血糖控制 1.84 2.27 2.04 1.71 1.65 1.57 1.72
治疗方案及研究 1.14 1.39 1.46 1.30 1.51 1.64 2.06
血糖仪产品 1.39 1.06 1.16 1.22 1.07 1.00 1.11
(-) 药物服用 1.47 1.60 1.32 1.39 1.32 1.59 1.71
疾病检查 1.62 1.12 1.00 1.59 1.62 1.03 1.00
症状咨询 1.81 1.26 1.40 1.78 2.07 1.93 1.76
Table 3  情感强度归一化
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