sentiment-shift（情绪转变）

Sentiment Shift Detector

Identify stocks where blogger sentiment has changed significantly.

Triggers

• "情绪变化最大的股票"
• "谁转向了"
• "sentiment shift"
• "who changed their mind"
• "态度转变"
• /sentiment-shift

Instructions

When the user wants to find sentiment shifts, follow these steps:

1. Get Multi-Day Summaries Call get_daily_summary for recent dates (today, yesterday, a few days ago) to compare sentiment over time.
2. Identify Changed Tickers Compare the summaries to find:

- Stocks that moved from bullish to bearish - Stocks that moved from bearish to bullish - Stocks with increased/decreased mentions

1. Get Detailed Sentiment For tickers with notable changes, call get_ticker_sentiment to understand who changed their view.
2. Search for Explanations Call search_viewpoints for changed tickers to find the reasoning behind sentiment shifts.
3. Present Results Format the output as: ## 情绪转变追踪 🔄 ### 转向看涨 📈 #### TICKER1 - **变化**: 看跌 → 看涨 - **时间**: X天前开始转变 - **关键转变博主**: 博主A, 博主B - **转变原因**: [摘要为什么改变看法] - **代表观点**: "[具体观点]" — 博主A ### 转向看跌 📉 #### TICKER2 - **变化**: 看涨 → 看跌 - **时间**: X天前开始转变 - **关键转变博主**: 博主C - **转变原因**: [摘要为什么改变看法] - **代表观点**: "[具体观点]" — 博主C ### 热度变化 🌡️ | 股票 | 之前提及 | 现在提及 | 变化 | |------|----------|----------|------| | XXX | 5 | 25 | ⬆️ +400% | | YYY | 20 | 3 | ⬇️ -85% | ### 分析 [总结市场情绪变化的整体趋势]

Tool Sequence

1. get_daily_summary(date=today) + get_daily_summary(date=yesterday) + get_daily_summary(date=3_days_ago) → Compare over time
2. Identify tickers with changed sentiment
3. get_ticker_sentiment(changed_ticker) → For each changed ticker
4. search_viewpoints(changed_ticker) → Find reasoning
5. Compile shift analysis

Notes

• Sentiment shifts can be leading indicators
• A blogger changing their view is often more significant than new bloggers joining
• Track both direction changes and intensity changes
• Include context for why shifts happened (news, earnings, etc.)