| 基于改进BTM模型的医疗服务质量因素识别 |
| |
| 引用本文： | 高慧颖,公孟秋,于思佳.基于改进BTM模型的医疗服务质量因素识别[J].北京理工大学学报,2022,42(11):1167-1174. |
| |
| 作者姓名： | 高慧颖 公孟秋 于思佳 |
| |
| 作者单位： | 北京理工大学 管理与经济学院，北京 100081 |
| |
| 基金项目： | 国家自然科学基金资助项目（71972012） |
| |
| 摘 要： | 针对在线医疗评论文本长度短、语义稀疏的特点，提出一种基于词共现分析的在线医疗评论主题挖掘模型。应用于短文本的BTM主题模型在词对的选择过程中缺少对词语语义相关性的考虑，通过引入词共现分析计算语义相关性，设定阈值筛选参与训练的词对，进行医疗评论主题挖掘，基于主题一致性TC值和JS散度对比改进的COA-BTM主题模型与传统的BTM主题模型和LDA主题模型在医疗评论主题挖掘中的效果。实验结果表明改进的COA-BTM模型在主题一致性和主题质量上均具有更好的效果，证明了其在在线医疗评论挖掘领域的有效性。基于改进算法在医疗评论主题挖掘中的应用和SERVQUAL模型，更全面地识别了医疗服务质量影响因素。
|
| 关 键 词： | 主题模型 在线医疗评论 词共现分析 COA-BTM模型 |
| 收稿时间： | 2021-12-15 |
Identification of Medical Service Quality Factors Based on COA-BTM Model |
| |
| Affiliation: | School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China |
| |
| Abstract: | Aiming at short text and sparse semantics of online medical reviews, an improved biterm topic model (BTM) topic mining model was proposed based on word co-occurrence analysis (COA) for online medical reviews. Due to the lack of semantic relevance consideration when BTM topic model was used to select word pairs in short texts, a word co-occurrence analysis method was introduced to calculate the semantic relevance, and thresholds were set to screen the participating word pairs for topic mining. Comparing with the traditional BTM and LDA topic models in the topic consistency TC value and JS divergence, the effect of improved COA-BTM was put up in medical review mining. The experiment results show that the improved COA-BTM model can provide a better result in topic consistency and topic quality, proving its effectiveness in the field of online medical review mining. Based on the mining results of this algorithm and SERVQUAL model, the medical service quality factors can be identified more comprehensively. |
| |
| Keywords: | |
|
| 点击此处可从《北京理工大学学报》浏览原始摘要信息 |
|
点击此处可从《北京理工大学学报》下载全文 |
|
