CarryChang / Customer_satisfaction_analysis
Licence: apache-2.0
基于在线民宿 UGC 数据的意见挖掘项目，包含数据挖掘和NLP 相关的处理，负责数据采集、主题抽取、情感分析等任务。目的是克服用户打分和评论不一致，实时对在线民宿的满意度评测，包含在线评论采集和情感可视化分析。搭建了百度地图POI查询入口，可以进行自动化的批量查询 POI 信息的功能；构建了基于在线民宿语料的 LDA 自动主题聚类模型，利用主题中心词能找出对应的主题属性字典；以用户打分作为标注，然后 litNlp 自带的字符级 TextCNN 进行情感分析，将情感分类概率分布作为情感趋势，最后通过 POI 热力图的方式对不同地域的民宿满意度进行展示。软件版本请见链接。
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python
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Customer_satisfaction_Analysis
结果整合
Demo 演示
基于用户 UGC 的在线民宿满意度挖掘，负责数据采集、主题抽取、情感分析等任务。开发的目的是克服用户打分和评论不一致，实现了在线评论采集和用户满意度分析。
主要功能包括在线原始评论采集、主题聚类、评论情感分析与结果可视化展示等四个模块，如下所示。
- 提取后的民宿地址和在线评论等信息如下。
- 搭建了百度地图 POI 查询入口，可以进行自动化的批量查询地理信息。
- 通过高频词可视化展示，归纳出评论主题。
- 构建了基于在线民宿语料的 LDA 自动化主题聚类模型，利用主题中心词能找出对应的主题属性字典，并使用用户打分作为标注，然后通过多种分类模型，选用最优模型对提出的评价主体 进行情感分析，针对主题属性表进行主题提取后的文本进行情感分析，分别得出当前主题对应的情感趋势，横坐标为所有关于主题为“环境”的情感得分，纵坐标为对应的情感的条数，可以起到纵观当前“环境”主题下的情感趋势，趋势往右代表当前主题评价较好，总共有{“交通”，“价格”，“体验”，“服务”，“特色”，“环境”，“设施”，“餐饮”}的主题，选取“环境”主题进行可视化之后的结果如下图所示。
- 通过POI热力图的方式对在线民宿满意度进行展示。
- 代码结构如下。
新版本特性
- 使用 litNLP 深度情感推理
- 增加多进程提高多个 topic 下的文本匹配速度
- Project_Main.py 直接完成细粒度情感极性可视化操作
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