| import datetime | |
| import traceback | |
| def keyword_prompt(video_info, summarization): | |
| keyword_prompt = f""" | |
| You are given a summary of a YouTube video. | |
| Your task is to identify the **main subject (person, company, or concept)** that the video is about. | |
| Only return a **single keyword** (preferably a named entity such as a person, brand, or organization). | |
| Video Info: | |
| {video_info} | |
| Video Summary: | |
| {summarization} | |
| Return only one keyword that best represents the **main focus** of the video content. | |
| """ | |
| return keyword_prompt | |
| def analysis_prompt(video_info, summarization, news, comments_text): | |
| analysis_prompt = f""" | |
| Analyze YouTube video information, summary, comments, and related latest news to create a Markdown format report. | |
| Video Info: {video_info} | |
| Video Summary: | |
| {summarization} | |
| Latest News: | |
| {news} | |
| Comments: | |
| {comments_text} | |
| Please write in the following format: | |
| # 🎬 YouTube Video Analysis Report | |
| ## 📌 Key Keywords | |
| `keyword` | |
| ## 🎯 Video Overview | |
| [Summary of main video content] | |
| ## 💬 Comment Sentiment Analysis | |
| ### 📊 Sentiment Distribution | |
| - **Positive**: X% | |
| - **Negative**: Y% | |
| - **Neutral**: Z% | |
| ### 🔍 Key Comment Insights | |
| 1. **Positive Reactions**: [Summary of main positive comments] | |
| 2. **Negative Reactions**: [Summary of main negative comments] | |
| 3. **Core Issues**: [Main topics found in comments] | |
| ### 🔍 Comments | |
| 1. Positive Comments: [Positive comments with sentiment classification and reasoning] | |
| 2. Negative Comments: [Negative comments with sentiment classification and reasoning] | |
| 3. Neutral Comments: [Neutral comments with sentiment classification and reasoning] | |
| ## 📰 Latest News Relevance | |
| [Analysis of correlation between news and video/comments] | |
| ## 💡 Key Insights | |
| 1. [First major finding] | |
| 2. [Second major finding] | |
| 3. [Third major finding] | |
| # ## 🎯 Business Intelligence | |
| # ### Opportunity Factors | |
| # - [Business opportunity 1] | |
| # - [Business opportunity 2] | |
| # ### Risk Factors | |
| # - [Potential risk 1] | |
| # - [Potential risk 2] | |
| # ## 📈 Recommended Actions | |
| # 1. **Immediate Actions**: [Actions needed within 24 hours] | |
| # 2. **Short-term Strategy**: [Execution plan within 1 week] | |
| # 3. **Long-term Strategy**: [Long-term plan over 1 month] | |
| --- | |
| **Analysis Completed**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | |
| """ | |
| return analysis_prompt | |
| def analysis_prompt(video_info, summarization, news, comments_text): | |
| analysis_prompt = f""" | |
| Analyze YouTube video information, summary, comments, and related latest news to create a Markdown format report. | |
| Video Info: {video_info} | |
| Video Summary: | |
| {summarization} | |
| Latest News: | |
| {news} | |
| Comments: | |
| {comments_text} | |
| Please write in the following format: | |
| # 🎬 YouTube Video Analysis Report | |
| ## 📌 Key Keywords | |
| `keyword` | |
| ## 🎯 Video Overview | |
| [Summary of main video content] | |
| ## 💬 Comment Sentiment Analysis | |
| ### 📊 Sentiment Distribution | |
| - **Positive**: X% | |
| - **Negative**: Y% | |
| - **Neutral**: Z% | |
| ### 🔍 Key Comment Insights | |
| 1. **Positive Reactions**: [Summary of main positive comments] | |
| 2. **Negative Reactions**: [Summary of main negative comments] | |
| 3. **Core Issues**: [Main topics found in comments] | |
| ### 🔍 Comments | |
| 1. Positive Comments: [Positive comments with sentiment classification and reasoning] | |
| 2. Negative Comments: [Negative comments with sentiment classification and reasoning] | |
| 3. Neutral Comments: [Neutral comments with sentiment classification and reasoning] | |
| ## 📰 Latest News Relevance | |
| [Analysis of correlation between news and video/comments] | |
| ## 💡 Key Insights | |
| 1. [First major finding] | |
| 2. [Second major finding] | |
| 3. [Third major finding] | |
| # ## 🎯 Business Intelligence | |
| # ### Opportunity Factors | |
| # - [Business opportunity 1] | |
| # - [Business opportunity 2] | |
| # ### Risk Factors | |
| # - [Potential risk 1] | |
| # - [Potential risk 2] | |
| # ## 📈 Recommended Actions | |
| # 1. **Immediate Actions**: [Actions needed within 24 hours] | |
| # 2. **Short-term Strategy**: [Execution plan within 1 week] | |
| # 3. **Long-term Strategy**: [Long-term plan over 1 month] | |
| --- | |
| **Analysis Completed**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | |
| """ | |
| return analysis_prompt | |
| def error_message(video_id): | |
| error_msg = f""" | |
| # ❌ Analysis Failed | |
| **Error Message:** {str(e)} | |
| **Debug Information:** | |
| - Video ID: {video_id} | |
| - Time: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | |
| **Check Items:** | |
| 1. Verify YouTube Video ID is correct | |
| 2. Verify API key is valid | |
| 3. Check network connection | |
| **Detailed Error:** | |
| ``` | |
| {traceback.format_exc()} | |
| ``` | |
| """ | |
| return error_msg | |
| def analysis_prompt2(content_type, all_comments ): | |
| analysis_prompt = f""" | |
| Please analyze the sentiment of the following {content_type} comments in detail: | |
| {all_comments} | |
| Please write detailed analysis results in the following format: | |
| ### 📊 Sentiment Distribution | |
| - **Positive**: X% (specific numbers) | |
| - **Negative**: Y% (specific numbers) | |
| - **Neutral**: Z% (specific numbers) | |
| ### 🔍 Sentiment-based Comment Analysis | |
| #### 😊 Positive Comments | |
| **Representative Comment Examples:** | |
| - "Actual comment 1" → Reason for positive classification | |
| - "Actual comment 2" → Reason for positive classification | |
| - "Actual comment 3" → Reason for positive classification | |
| **Main Positive Keywords:** keyword1, keyword2, keyword3 | |
| #### 😡 Negative Comments | |
| **Representative Comment Examples:** | |
| - "Actual comment 1" → Reason for negative classification | |
| - "Actual comment 2" → Reason for negative classification | |
| - "Actual comment 3" → Reason for negative classification | |
| **Main Negative Keywords:** keyword1, keyword2, keyword3 | |
| #### 😐 Neutral Comments | |
| **Representative Comment Examples:** | |
| - "Actual comment 1" → Reason for neutral classification | |
| - "Actual comment 2" → Reason for neutral classification | |
| **Main Neutral Keywords:** keyword1, keyword2, keyword3 | |
| ### 💡 Key Insights | |
| 1. **Sentiment Trends**: [Overall sentiment trend analysis] | |
| 2. **Main Topics**: [Most mentioned issues in comments] | |
| 3. **Viewer Reactions**: [Main interests or reactions of viewers] | |
| ### 📈 Summary | |
| **One-line Summary:** [Summarize overall comment sentiment and main content in one sentence]""" | |
| return analysis_prompt | |
| def channel_markdown_result(videos, total_video_views, avg_video_views, videos_text, shorts, total_shorts_views, avg_shorts_views, shorts_text, video_sentiment, shorts_sentiment): | |
| markdown_result = f"""# 📊 YouTube Channel Analysis Report | |
| ## 🎬 Latest Regular Videos ({len(videos)} videos) | |
| **Total Views**: {total_video_views:,} | **Average Views**: {avg_video_views:,.0f} | |
| {videos_text} | |
| --- | |
| ## 🎯 Latest Shorts ({len(shorts)} videos) | |
| **Total Views**: {total_shorts_views:,} | **Average Views**: {avg_shorts_views:,.0f} | |
| {shorts_text} | |
| --- | |
| ## 💬 Comment Sentiment Analysis | |
| ### 📺 Regular Video Comment Reactions | |
| {video_sentiment} | |
| ### 📱 Shorts Comment Reactions | |
| {shorts_sentiment} | |
| --- | |
| ## 💡 Key Insights | |
| - **Regular Video Average**: {avg_video_views:,.0f} views | |
| - **Shorts Average**: {avg_shorts_views:,.0f} views | |
| - **Performance Comparison**: {"Regular videos perform better" if avg_video_views > avg_shorts_views else "Shorts perform better" if avg_shorts_views > avg_video_views else "Similar performance"} | |
| --- | |
| **Analysis Completed**: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | |
| """ | |
| return markdown_result |