xiaofei030

xiaofei030 / campus_sentiment_analysis

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一款基于 多智能体 + MCP + Skill 架构的校园舆情监测与情感分析平台，面向辅导员和学生管理人员。旨帮助学校更好地了解学生心声，提供更贴心的服务，切实解决同学们关切的问愿。

19
0
89% credibility
Found Mar 08, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A web platform that monitors social media for school-related discussions, performs sentiment analysis and risk detection, and provides dashboards, alerts, and reports for counselors.

How It Works

1
🎓 Discover the campus mood tracker

You hear about a helpful tool that watches social chats about your school to spot student feelings early.

2
📥 Get it ready on your computer

Download the package and prepare it with a few simple steps so it's all set up for your school.

3
🔗 Link your smart helper

Connect an AI thinking service and your school's info storage so it understands your world perfectly.

4
🧪 Test with sample posts

Add some pretend student messages to see how it spots happy, sad, or worried vibes right away.

5
🚀 Open the dashboard

Click to launch the colorful screen full of charts and insights about campus feelings.

6
Choose how to watch
🌡️
Scan hot trends

Let it grab popular posts from big sites and filter for your school automatically.

📝
Hunt your keywords

Type school words like cafeteria or exams to deeply search chats on your favorite sites.

7
📊 See moods and warnings

Watch live charts of happy/sad trends, get alerts for worries, and check detailed breakdowns.

✅ Catch concerns early

Now you stay ahead of student stresses with reports and insights to help everyone thrive.

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Star Growth

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AI-Generated Review

What is campus_sentiment_analysis?

This Python platform monitors campus sentiment by crawling social media like Weibo, Zhihu, and Bilibili for school-specific discussions, running sentiment analysis, and flagging risks like student anxiety or complaints. It delivers a React dashboard with trends, alerts, and reports for counselors to spot issues early and respond. Built on FastAPI, LangChain agents, and DeepSeek, it handles data collection, multi-model analysis, and MySQL storage out of the box.

Why is it gaining traction?

Its multi-agent setup coordinates sentiment, topic, and risk screening with coordinator agents, plus an MCP server that lets tools like Cursor or Claude Desktop call campus sentiment analysis directly via mcp github copilot vscode integration. The skill system adds pluggable modules for alerts and reports, and keyword-based deep crawling beats generic scrapers by focusing on custom campus terms. Developers dig the ready-to-run API endpoints for batch analysis and live dashboards without wiring up crawlers themselves.

Who should use this?

University counselors tracking dorm or cafeteria gripes on social platforms, student affairs admins needing risk alerts for mental health trends, or school IT teams building internal monitoring without starting from scratch. Ideal for Chinese campuses given its Weibo/Tieba focus, or anyone extending it via MCP skills for custom workflows.

Verdict

Grab it if you're in student management—solid docs, test data generator, and Swagger API make setup fast despite 19 stars signaling early maturity. Credibility score of 0.9% reflects low adoption, but the MCP + skill architecture positions it well for growth; fork and tweak for production.

(198 words)

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