一个比较基础、全面的文本挖掘过程。包含了利用机器学习和文本挖掘技术完成情感分析模型搭建；利用情感极性判断与程度计算来判断情感倾向；利用词频和TF-IDF挖掘出正负文本中的关键点情况；利用文本挖掘相关算法找到平台中用户讨论的集中点。
active 2023-09-22 → 2026-04-09 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 931 days from 2023-09-22 to 2026-04-09. Pushes: 4 total, peak 2 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 1 total, peak 1 in a day. Comments: 0 total, peak 0 in a day. Stars: 32 total, peak 2 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| MatoYing | 5 | 4 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
- Issue#2MatoYing2024-02-08 11:35请问数据是怎么获取的呢
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 32 stars here means stars gained during the window, not the repo's star count.