ChatGPT answers, unedited

For that analysis, a sentiment-analysis + video-analytics workflow is the best fit.

You’d want a tool/setup that can:

• Analyze comment sentiment (positive/neutral/negative) over time.
• Track video metrics such as views, watch time, likes, shares, and retention.
• Align both datasets by date/video.
• Calculate correlations between sentiment shifts and performance changes.
• Visualize the relationship with time-series and scatter charts.

If you're talking about YouTube comments and video performance specifically, I can help identify the best tool for doing this end-to-end.

For that kind of analysis, a data-analysis tool is the best fit—ideally one that can combine:

• Comment text → sentiment scores over time
• Video metrics → views, watch time, likes, shares, retention, etc.
• Time alignment → compare sentiment changes with performance changes
• Correlation/regression → quantify whether sentiment is associated with performance
• Visualization → trend lines and sentiment-vs-performance scatter plots

If you have the data in a CSV/Excel file, I can help you analyze it directly here and produce the trend/correlation analysis.

For analyzing comment sentiment trends and how they correlate with video performance over time, you’d typically want a combination of tools rather than a single one. Good options depend on where your videos live:

If your videos are on YouTube

• YouTube Analytics — tracks views, watch time, retention, engagement, likes, comments, and traffic sources.
• YouTube Data API — useful for pulling historical comments and video metrics into your own analysis pipeline.
• A sentiment analysis tool can classify comments (positive/negative/neutral), detect themes, and measure changes over time.

For sentiment analysis

• Google Cloud Natural Language AI — analyzes sentiment and entities in text at scale.
• AWS Comprehend — provides sentiment detection and topic-related analysis.
• Hugging Face Models — offers many open-source sentiment models you can customize.

For dashboards and correlation analysis

• Tableau — good for dashboards showing sentiment score vs. views, retention, subscribers, etc.
• Microsoft Power BI — similar dashboarding and analytics workflows.
• Looker Studio — lightweight option for combining analytics data sources.

A typical workflow would look like:

1. Pull video metrics:
   • views over time
   • watch time
   • average view duration
   • likes/dislikes (where available)
   • subscriber changes
   • comment volume

2. Pull comments:
   • timestamp
   • author (if needed)
   • comment text
   • likes/replies

3. Run sentiment analysis:
   • sentiment score (e.g., -1 to +1)
   • emotion/topic categories
   • recurring themes

4. Join datasets by:
   • video ID
   • date/week/month

5. Analyze:
   • “Do videos with more positive comments get higher retention?”
   • “Does a spike in negative sentiment predict drops in views?”
   • “Which topics drive engagement?”

For a no-code approach, a stack like YouTube Analytics + Google Sheets/BigQuery + Looker Studio + a sentiment API is often enough. For a larger creator operation, YouTube Data API + Python/R + a sentiment model + a BI dashboard gives much deeper trend analysis.

For that use case, you’d want a social/video analytics tool that can combine:

• Comment sentiment analysis — positive/negative/neutral sentiment over time
• Topic/keyword trends — what viewers are talking about
• Video performance — views, watch time, likes, shares, engagement rate
• Time-series correlation — whether changes in sentiment or topics coincide with changes in performance

If you’re analyzing YouTube, tools in the YouTube analytics/social-listening space are the best fit. I can also help you identify the best specific tool depending on whether you need YouTube, TikTok, Instagram, or multiple platforms.

For that workflow, a social/video analytics platform is the best fit—ideally one that combines:

• Comment sentiment analysis over time (positive/neutral/negative, topics, spikes)
• Video performance metrics (views, watch time, retention, likes, shares, subscribers)
• Time-series correlation so you can see whether sentiment changes track with performance changes
• Export/API access if you want to run deeper statistical analysis

If you're working with YouTube, YouTube Analytics plus comment data can support this, with a separate sentiment-analysis layer. For a more automated setup, tools such as Sprout Social, Brandwatch, or Talkwalker are worth considering.

If you tell me which platform your videos are on (YouTube, TikTok, Instagram, etc.), I can recommend the specific tool and workflow that would best let you correlate sentiment with performance.

For that workflow, YouTube Analytics is the most natural starting point for video performance, paired with a sentiment-analysis tool for the comments.

If you want an all-in-one analysis, I can help you set up a workflow that:

• Pulls comments over time
• Classifies sentiment (positive/neutral/negative)
• Tracks views, watch time, likes, CTR, etc.
• Aligns sentiment with performance by date/video
• Calculates correlations and highlights meaningful changes
• Visualizes sentiment vs. performance trends

If you're asking which tool available in this chat can do the analysis, I can help analyze the data once you provide/export the comments and video-performance data.

For that workflow, you want an analytics platform that combines audience/comment data with video-performance metrics over time.

A good setup is:

• Sentiment analysis → classify comments as positive, neutral, or negative and track themes/topics.
• Video analytics → track views, watch time, retention, likes, shares, CTR, etc.
• Time-series/correlation analysis → align sentiment with performance by day/week and test whether changes in sentiment coincide with changes in performance.
• Visualization → use a line chart for trends and a scatter chart for sentiment-vs-performance relationships.

If you're talking about a specific platform (e.g. YouTube, TikTok, Instagram) or a tool you already have access to, tell me which one and I can recommend the most appropriate tool/workflow.

For that specific job, a data-analysis/BI tool connected to your video analytics and comment data is the best fit.

You’d want something that can:

• Run sentiment analysis on comments over time.
• Track sentiment by video, upload date, topic, or audience segment.
• Correlate sentiment metrics with views, watch time, likes, shares, retention, and engagement.
• Plot both series on a timeline and calculate correlations/lagged relationships.
• Flag changes such as “negative sentiment increased 3 days after this video’s performance dropped.”

If you're talking about YouTube comments + YouTube video performance, I can recommend a specific tool/workflow (e.g. YouTube Analytics + a sentiment-analysis/BI platform) based on whether you want no-code, spreadsheet-based, or programmatic analysis.

For that workflow, you want a social/video analytics tool that combines comment sentiment with performance metrics over time.

A good setup should let you:

• Track positive/neutral/negative sentiment by day or week
• Compare sentiment against views, watch time, likes, shares, and engagement rate
• Identify whether sentiment changes precede or follow performance changes
• Break trends down by individual videos or content themes

If you tell me which platform you’re analyzing (e.g. YouTube, TikTok, Instagram) and where your data currently lives, I can point you to the best tool for it.

For that workflow, the best fit is an analytics/data-analysis tool that can combine two time series:

• Comment sentiment — positive/neutral/negative sentiment over time, ideally by video.
• Video performance — views, watch time, likes, shares, retention, engagement rate, etc.
• Correlation analysis — test whether changes in sentiment are associated with changes in performance, including lagged effects (e.g., sentiment in week 1 vs. views in week 2).
• Visualization — time-series and scatter plots to spot trends and relationships.

If you're asking which connected tool available to me can do this, I can check your available plugins and find the best one for your data source (YouTube, TikTok, Instagram, CSV, etc.).

For that specific analysis, a YouTube analytics/data-analysis tool is the best fit—ideally one that can combine:

• Comment sentiment over time: positive/neutral/negative sentiment, topics, recurring phrases
• Video performance: views, watch time, likes, comments, shares, CTR, retention
• Time alignment: compare sentiment changes against performance changes by day/week
• Correlation analysis: test whether sentiment is associated with views, engagement, or retention, while avoiding assuming correlation means causation

If you have the data in a spreadsheet/CSV, I can also analyze it directly and produce trend and correlation charts.

If you mean which connected ChatGPT plugin/tool you have available, I can help identify the right one if you tell me what platform your video comments are on (e.g. YouTube, TikTok, Instagram).

For that, you’d want an analytics tool that can combine comment text with time-series video metrics. The workflow is typically:

1. Collect comments over time.
2. Run sentiment analysis (positive/neutral/negative, ideally with topic labels).
3. Pull video performance metrics for the same periods—views, watch time, likes, shares, retention, etc.
4. Join them by video + date/time.
5. Analyze correlations and lagged relationships—for example, whether a spike in negative sentiment precedes a drop in retention or whether positive comments follow unusually strong videos.
6. Visualize the trends with line charts and sentiment-vs-performance scatter plots.

If you're asking which tool in this environment can help: the chart widget can visualize the resulting time trends and correlations, but you'd need a data source/analytics connector to actually retrieve and analyze the comments and video metrics. If you tell me the platform (e.g. YouTube, TikTok, Instagram), I can point you to the best tool/workflow for getting that data.

To analyze comment sentiment trends and correlate them with video performance over time, you’ll typically want a combination of a video analytics platform, sentiment analysis/NLP tool, and a data visualization or BI tool.

Useful tool categories:

1. Video analytics platforms

These provide views, watch time, retention, likes, comments, shares, and other performance metrics.

• YouTube Studio — for YouTube videos: impressions, CTR, watch time, audience retention, comments, and engagement metrics.
• TikTok Analytics — for TikTok performance and audience insights.
• Meta Business Suite — for Facebook/Instagram video analytics.

2. Sentiment analysis tools

These ingest comments and classify them by sentiment (positive, neutral, negative), themes, emotions, or topics.

• Google Cloud Natural Language AI — sentiment scoring and entity/topic analysis.
• AWS Comprehend — sentiment and text analytics at scale.
• Azure AI Language — sentiment, opinions, and key phrase extraction.

3. Data analysis and dashboards

Use these to join sentiment data with video metrics and find patterns like:

• “Videos with rising positive sentiment in the first 24 hours get higher retention.”
• “Negative comments spike after a specific content change.”
• “Certain topics drive more engagement.”

Common choices:

• Tableau
• Microsoft Power BI
• Looker Studio

4. Social listening platforms (all-in-one options)

These are designed specifically for tracking audience reactions over time:

• Brandwatch
• Sprout Social
• Meltwater

A typical workflow would be:

1. Export comments + timestamps from your video platform.
2. Run comments through sentiment/topic analysis.
3. Aggregate sentiment by day/week/video.
4. Join with views, watch time, retention, likes, and shares.
5. Build a dashboard showing sentiment vs. performance trends.

For a creator or marketing team, a practical stack is often YouTube Studio + sentiment analysis API + Looker Studio/Tableau. For a large content operation monitoring many channels, a social listening platform may be more efficient.

For that specific job, you want an analytics tool that can combine two time series:

• Comment sentiment: positive/neutral/negative sentiment by day/week/month, ideally with topic or keyword breakdowns.
• Video performance: views, watch time, likes, shares, engagement rate, retention, etc.
• Correlation analysis: compare sentiment changes against performance changes, including possible lag effects (e.g., sentiment spikes preceding view spikes).
• Visualization: a line chart for trends and a scatter/correlation view for sentiment vs. performance.

If you're analyzing YouTube videos, YouTube Analytics can provide the performance side, while a sentiment-analysis tool or a data-analysis environment can process the comments. For a more automated end-to-end workflow, I can also look for a plugin/tool available in ChatGPT that can ingest your comment and video-performance data and perform the correlation analysis.