本文使用携程平台的景区游客评论进行文本挖掘，生成了多个变量来反映评论的“内蕴价值”，包括文本相似 度、情感得分、词性分布、评论长度等，并系统地分析了不同变量的作用方式和效果。通过建立分类模型，本文分 析了它们与用户评分的相关程度与相对重要性，进而设计了基于评论指标优化的数据产品，来对景区内部精选评论 的选取排序及榜单的准入标准提供有效�
active 2024-09-01 → 2025-03-23 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 204 days from 2024-09-01 to 2025-03-23. Pushes: 4 total, peak 4 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 12 total, peak 2 in a day.
- Pushes
- Pull requests
- Issues
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- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| Wang-ZH-Stat | 4 | 4 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 12 stars here means stars gained during the window, not the repo's star count.