ChatGPT answers, unedited

If you want to analyze comment sentiment trends and correlate them with video performance over time, look for a tool that combines:

• Comment ingestion (comments + timestamps)
• Sentiment analysis (positive/negative/neutral or sentiment scores)
• Topic/theme clustering (what viewers are reacting to)
• Video analytics (views, watch time, engagement, growth curves)
• Historical dashboards (changes over weeks/months)

Some tools that fit this use case:

A common analytics setup is:

1. Pull video metrics:

• Views over time
• Watch time
• Average view duration
• Likes/comments per view
• Subscriber changes
• Pull comments:

• Comment date
• Likes/replies
• Comment text
• Run NLP analysis:

• Sentiment score by day/week
• Emerging topics
• Frequently mentioned issues or praise
• Correlate:

• “Sentiment improved 3 days after a format change”
• “Negative comments spiked while retention dropped”
• “Videos with higher positive-comment ratios gained more subscribers”

For a creator or channel team, I’d usually start with a YouTube-specific tool like CommentShark or Sentimerge, then export data into a dashboard tool (Looker Studio, Tableau, Power BI, or a spreadsheet) if you need custom correlations. sentimerge.com CommentShark

If you mean YouTube specifically, TikTok, Instagram, or a broader brand/social listening use case, the best choice changes.

For YouTube, a tool like CommentShark is a close fit: it tracks sentiment over time and provides per-video sentiment breakdowns alongside engagement patterns. CommentShark

For a more flexible workflow, Commentrix can export comments—including sentiment, timestamps, likes, and replies—to CSV/Excel/JSON, letting you correlate sentiment with views, watch time, CTR, or retention in a spreadsheet/BI tool. commentrix.co

Best workflow: sentiment by day/video → merge with YouTube Analytics metrics → calculate correlations and plot trends over time.

If you mean YouTube, the strongest setup is YouTube Analytics API + YouTube Data API + an NLP/sentiment tool.

• YouTube Analytics API — gives you time-based video performance metrics such as views, watch time, likes, comments, shares, and subscribers gained. Google for Developers Google for Developers
• YouTube Data API — can retrieve the actual comments, which you can run through a sentiment model (positive/neutral/negative) and optionally topic extraction. Google explicitly permits NLP-based sentiment analysis of comments. Google for Developers
• YouTube Studio Advanced Mode — useful if you want a lower-code approach; it lets you compare videos/time periods and export analytics data. Google Help
• A spreadsheet, Python, or BI tool — combine the comment sentiment by day/week with performance metrics and calculate correlations or lagged relationships.

A useful dataset would look roughly like:

Then you can examine questions such as whether sentiment changes precede changes in views, rather than simply correlating total comments with total views.

One caveat: YouTube Analytics API data typically has 48–72 hours of processing latency, so it isn't ideal for very recent performance monitoring. Google for Developers

If you tell me whether you want a no-code tool, Python workflow, or dashboard, I can recommend a specific tool stack for doing this end-to-end.

If you mean YouTube videos, you want a tool that combines two datasets:

1. Comment sentiment over time — positive/negative/neutral sentiment, themes, questions, complaints, etc.
2. Video performance over time — views, watch time/retention, CTR, likes, subscribers, and engagement.

A few options stand out:

• tubeanalytics.net — probably the closest match to your specific use case. It explicitly describes connecting audience sentiment with retention, CTR, and subscriber growth, plus trend alerts. TubeAnalytics
• audiency.app — tracks sentiment trends and engagement analytics, including comment velocity and activity patterns. Audiency
• sentimerge.com — strong if your priority is deep comment analysis; it supports sentiment, thematic clustering, and performance trends/channel intelligence. Sentimerge
• commentaction.com — useful for tracking how themes and sentiment change when you re-analyze videos over time. CommentAction

For actual correlation analysis

I'd favor TubeAnalytics or a custom data pipeline. The ideal analysis would produce something like:

“Videos where negative sentiment about the intro exceeds 20% have 15% lower average retention in the first 30 seconds.”

You'd correlate sentiment/theme scores by video and time period against CTR, retention, watch time, views, and subscriber conversion, rather than simply comparing total comments with views.

If you tell me whether you're analyzing your own YouTube channel, a competitor's channel, or hundreds of videos, I can recommend the best tool/workflow for that specific case.

For analyzing comment sentiment trends and correlating them with video performance over time, you’ll want a tool that combines:

• comment ingestion (comments + timestamps)
• sentiment classification
• topic/theme clustering
• video metrics (views, likes, engagement, retention if available)
• time-series dashboards or exports

Some good options:

• analytics.youtube.com + a sentiment analysis tool — best if you want official performance data (views, watch time, engagement) paired with separate AI analysis of comments.
• Ventress — combines video performance context with comment sentiment breakdowns and audience insights, useful for seeing whether high-performing videos also have healthier audience reactions. Ventress - Your YouTube Copilot
• Sentimerge — focuses on large-scale comment analysis, sentiment, themes, audience patterns, and tracking insights over time. Sentimerge
• CommentShark — provides sentiment timelines, per-video sentiment breakdowns, topic tagging, and anomaly detection for changes in audience mood. CommentShark
• YouResearch — useful if you want cross-video comparisons and trend discovery across many videos. YouResearch

For a more analytical workflow, many teams do:

1. Export video metrics from YouTube Analytics.
2. Export comments (via a comment analysis tool or API).
3. Run sentiment/topic modeling.
4. Join datasets by: - video ID
5. upload date
6. comment date
7. views/watch time/engagement
8. Plot trends such as: - sentiment score vs. views
9. negative comment spikes vs. drops in engagement
10. recurring themes in top-performing videos

If you need custom dashboards or predictive analysis, a stack like YouTube Data API + BigQuery/PostgreSQL + Python sentiment models + Looker Studio/Tableau is usually more flexible than creator-focused tools.

If you mean YouTube, the best fit is a combination of a comment-sentiment tool + YouTube performance analytics.

• sentimerge.com — probably the closest match to your exact use case. It analyzes large volumes of comments for sentiment and themes, and also advertises performance trends and channel tracking over time. Sentimerge
• ventress.app — combines video metrics (views, likes, comments, engagement context) with positive/negative/neutral/advisory comment sentiment, making it useful for comparing audience mood with performance. Ventress - Your YouTube Copilot
• channelgrade.com — interesting if you specifically want sentiment alongside performance metrics and trends, including CTR and retention. ChannelGrade
• youresearch.co — stronger for cross-video research: it can identify sentiment shifts and recurring themes across multiple videos. YouResearch

What I'd choose

For “Does audience sentiment predict or correlate with video performance over time?”, I'd start with Sentimerge or Ventress.

Ideally, you want a dataset like:

Then you can test correlations such as sentiment → retention, negative sentiment → views, or positive sentiment → engagement, while controlling for things like video topic and age.

If you tell me what platform your comments/videos are on (YouTube, TikTok, Instagram, etc.), I can recommend the best tool specifically for that platform.

For analyzing comment sentiment trends and correlating them with video performance over time, you’ll want a tool that combines:

• Comment ingestion (YouTube/TikTok/etc.)
• Sentiment classification over time
• Video metrics (views, likes, shares, watch time, engagement rate)
• Trend dashboards or exportable data for correlation analysis

Good options include:

A typical analysis workflow would look like:

1. Collect data

• Video ID
• Publish date
• Views/likes/comments/shares
• Every comment with timestamp
• Score comments

• Positive / neutral / negative sentiment
• Emotion (joy, anger, frustration, etc.)
• Topics/themes
• Create time-series metrics

• Daily sentiment score
• Sentiment change after upload
• Negative-comment spikes
• Topic trends
• Correlate with performance

Examples:

• Did videos with higher positive sentiment get more views?
• Do negative sentiment spikes predict lower retention?
• Which topics drive comments but hurt engagement?
• How long after posting does sentiment peak?

If you mainly analyze your own YouTube channel, I’d lean toward a YouTube analytics + sentiment workflow (Chattermill or a custom pipeline). If you analyze many creators/brands across TikTok, YouTube Shorts, Reels, etc., a video social intelligence platform like Syncly or dig is a better fit. Chattermill Syncly

For that workflow, you’d want a data-analysis/BI tool that can combine:

• Comment text → sentiment scores over time
• Video metrics → views, watch time, likes, shares, retention, etc.
• Time-series correlation → whether sentiment changes track or predict performance changes
• Segmentation → compare sentiment/performance by video, topic, date, or audience

If you're asking about a specific tool available in your connected apps, I can check which one is best suited for this.

For that workflow, you want an analytics/data-analysis tool that can combine:

• Comment sentiment over time — positive/negative/neutral or sentiment score
• Video performance — views, watch time, retention, likes, shares, subscribers, etc.
• Time-series correlation — e.g., whether sentiment spikes coincide with higher/lower views or engagement
• Segmentation — by video, topic, audience, or date

If you're working with a dataset (CSV/Excel) of comments plus video metrics, I can analyze it directly: calculate sentiment trends, correlations, lagged relationships, and create trend/scatter charts.

If you mean a specific existing tool/plugin in your connected apps, tell me where the comments and video analytics live (e.g. YouTube, TikTok, a spreadsheet, database), and I can identify the best available tool for it.

For analyzing comment sentiment trends and how they correlate with video performance over time, you’ll typically want a combination of tools rather than a single one. The best choice depends on your platform (YouTube, TikTok, Instagram, etc.) and how deep you want the analysis to go.

Tools that can help

• support.google.com + sentiment analysis tools

• Best if your videos are on YouTube.
• You can combine: - Views, watch time, retention, CTR, likes, and subscriber changes
• Comment exports
• Sentiment scoring (positive/negative/neutral)
• Useful for finding patterns like “videos with more positive comments retain viewers longer.”
• brandwatch.com

• Strong for social listening and large-scale sentiment tracking.
• Can monitor comment sentiment over time, detect spikes in reactions, and compare sentiment shifts against engagement metrics.
• sproutsocial.com

• Social media management platform with analytics and listening features.
• Useful for tracking audience sentiment alongside content performance.
• hootsuite.com

• Helps combine social engagement metrics with audience sentiment monitoring.
• talkwalker.com

• Good for trend detection, sentiment changes, and identifying what topics drive engagement.
• Python + analytics stack (for custom analysis)

• Best if you want a tailored model: - Pull comments via platform APIs
• Run sentiment analysis using models such as VADER, spaCy, or transformer-based models
• Join sentiment data with video metrics
• Visualize trends in tools like Tableau, Power BI, or Looker Studio

A typical analysis workflow

1. Collect video metrics

• Views over time
• Watch time
• Retention curve
• Likes/dislikes
• Shares
• Subscribers gained
• Collect comments

• Timestamp each comment
• Analyze sentiment
• Extract themes/topics
• Track recurring complaints or praise
• Correlate

Examples:

• Did positive sentiment spikes happen before view growth?
• Do negative comments predict lower retention?
• Which topics generate the strongest engagement?
• Do certain creators/topics produce more loyal audiences?
• Visualize

• Sentiment score over time vs. views
• Comment volume vs. watch time
• Topic clusters vs. subscriber growth

If you tell me the platform (YouTube, TikTok, Instagram, Reddit, etc.) and whether you need a no-code tool or a custom data pipeline, I can suggest a more specific setup.

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

• Comment sentiment: positive/neutral/negative trends and recurring themes
• Video performance: views, watch time, engagement, likes, shares, retention
• Time-series correlation: compare sentiment changes against performance changes by day/week/video
• Segmentation: identify whether sentiment differs for high- vs. low-performing videos

If you're working with YouTube specifically, tools such as YouTube Analytics + a sentiment-analysis workflow can do this well. For a more automated setup, you could export comments and performance data into a BI tool such as Tableau or Power BI and analyze correlations over time.

If you tell me which platform your videos are on (YouTube, TikTok, Instagram, etc.), I can recommend the most suitable tool and workflow.

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

• Comment sentiment over time
• Engagement metrics (likes, replies, shares)
• Video performance (views, watch time, retention)
• Time-series correlation between sentiment changes and performance changes

If you mean a specific platform like YouTube, TikTok, or Instagram, tell me which one and I can point you to the best tool/workflow for it.

For that specific analysis, a social-media/video analytics tool is the best fit—ideally one that combines:

• Comment sentiment analysis over time
• Views, watch time, likes, shares, and engagement metrics
• Time-series comparisons between sentiment and performance
• Correlation or regression analysis to identify whether sentiment changes are associated with performance changes

If you're working with YouTube, for example, you could combine YouTube Analytics data with comment-level sentiment analysis in a BI tool such as Power BI or Tableau.

If you tell me which platform you're analyzing (YouTube, TikTok, Instagram, etc.), I can recommend the most appropriate tool and workflow.

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

Useful options:

• YouTube Analytics (for YouTube videos)

• Tracks views, watch time, retention, likes, subscribers gained, traffic sources, and performance over time.
• Pair it with comment exports/API data to compare sentiment shifts against spikes or drops in engagement.
• YouTube Data API + sentiment analysis tools

• Pull comments over time, then score them for positive/negative/neutral sentiment.
• Common analysis tools: - cloud.google.com — sentiment scoring and entity analysis.
• platform.openai.com — can classify comments, summarize themes, detect recurring complaints/praise, and identify changes in audience mood.
• Social listening platforms (best for multiple platforms)

• brandwatch.com — tracks sentiment, topics, and audience reactions across channels.
• sproutsocial.com — combines social engagement analytics with sentiment and reporting.
• meltwater.com — useful for broader media and social monitoring.
• BI/dashboard tools (for trend correlation)

• tableau.com or powerbi.microsoft.com can plot: - sentiment score over time
• views/watch time over time
• comment volume
• engagement rate
• topic changes

A typical workflow:

1. Export video metrics:

• views/day
• watch time
• average retention
• likes/dislikes (where available)
• subscriber changes
• Collect comments:

• timestamp each comment
• analyze sentiment
• extract themes (e.g., “love the editing,” “audio quality issue”)
• Join the datasets by date:

• Example insight: - “Positive sentiment increased 25% after the creator changed thumbnails, and average watch time increased 18%.”
• “Negative comments about pacing appeared before retention dropped.”

For a creator with a small-to-medium number of videos, a practical stack would be:
YouTube Analytics + YouTube API + OpenAI sentiment/topic analysis + Google Sheets/Power BI dashboard.

For a large channel or many platforms, a dedicated social listening platform is usually more efficient.

For that workflow, a data analytics tool that combines social/comment data with video metrics is the best fit. You’d want to track:

• Comment sentiment (positive/neutral/negative) over time
• Views, watch time, likes, shares, and engagement rate
• Sentiment changes around specific videos or publishing dates
• Correlations between sentiment and performance metrics
• Outliers—for example, videos with unusually positive sentiment and unusually high watch time

If you're working with YouTube data specifically, I can recommend the best tools for automatically analyzing comments + video performance together.

For that kind of analysis, a data analytics/BI tool is the best fit—something that can combine comment-level sentiment data with video metrics such as views, watch time, likes, and engagement over matching time periods.

A typical workflow would be:

• Sentiment analysis: classify comments as positive, neutral, or negative and track the mix over time.
• Video performance: pull views, watch time, retention, likes, shares, and subscriber/follower growth.
• Correlation: compare sentiment changes against performance changes by day/week or by individual video.
• Visualization: build a dashboard showing sentiment and performance trends together.

If you're talking specifically about YouTube comments and video performance, I can recommend the best tool stack for that (including options that require little/no coding).

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

Good options:

• support.google.com — Best starting point for YouTube videos. It provides performance metrics over time (views, watch time, retention, likes, engagement, audience data). Pair exported data with sentiment analysis of comments.
• developers.google.com — Useful if you want to automatically collect comments, timestamps, likes, replies, and video statistics for analysis pipelines.
• cloud.google.com — Can analyze comment sentiment, entities, and emotional tone at scale.
• aws.amazon.com — Another option for automated sentiment classification and trend analysis across large comment datasets.
• huggingface.co — Useful if you want customizable sentiment models (for example, models tuned for social media language, slang, or specific niches).
• tableau.com or microsoft.com — Good for dashboards showing relationships such as:

• average sentiment score vs. views
• sentiment changes after uploads
• positive/negative comment spikes vs. retention changes
• topics driving engagement

A typical workflow would look like:

1. Pull video metrics:

• views by day/week
• watch time
• average view duration
• likes/dislikes (where available)
• subscriber changes
• Collect comments:

• comment text
• date posted
• likes/replies
• commenter engagement
• Run sentiment analysis:

• positive/neutral/negative classification
• sentiment score over time
• recurring themes/topics
• Join the datasets:

• Example insight: “Videos with sentiment scores above 0.6 in the first 48 hours tend to get 30% higher watch-time growth.”

For a no-code approach, a stack like YouTube Analytics + a social listening tool + Power BI/Tableau is usually easiest. For a custom research pipeline, use the YouTube Data API + a sentiment model + Python/R + a dashboard tool.

For that workflow, a data-analysis tool is the best fit—something that can ingest your comment data and video metrics, then calculate sentiment over time and correlate it with views, watch time, engagement, etc.

A typical analysis would combine:

• Comment sentiment: positive/neutral/negative sentiment by day/week
• Video performance: views, likes, comments, shares, watch time, retention
• Time-series comparison: whether sentiment changes precede or follow performance changes
• Correlation: Pearson/Spearman correlations between sentiment and performance metrics
• Segmentation: compare sentiment trends across individual videos, topics, or publishing periods

If you're asking which tool available in this chat can do that, I can help analyze the data if you provide/upload the comments and performance metrics (CSV, spreadsheet, etc.).

For that analysis, you’d want a social-media/video analytics tool that can combine:

• Comment sentiment over time (positive/negative/neutral)
• Comment volume and themes
• Video performance (views, watch time, likes, shares, retention)
• Time-series correlation between sentiment shifts and performance changes

If you mean a tool available inside ChatGPT, tell me which platform you’re analyzing (e.g., YouTube, TikTok, Instagram), and I can identify the best available tool/workflow.

To analyze comment sentiment trends and correlate them with video performance over time, you’ll typically want a combination of a social/video analytics platform and a sentiment analysis tool. The best choice depends on where your videos live (YouTube, TikTok, Instagram, etc.) and how deep you want the analysis to go.

Some useful options:

• YouTube Studio — Best starting point for YouTube creators. It provides video performance metrics over time (views, watch time, retention, engagement, traffic sources). It does not provide deep built-in sentiment trend analysis, so you’d usually pair it with another tool.
• Brandwatch — Strong for large-scale social listening. It can analyze comment sentiment, detect themes/topics, track sentiment shifts over time, and compare those trends against campaign or content performance metrics.
• Sprout Social — Useful for social media teams managing multiple platforms. It combines engagement analytics, audience insights, and sentiment reporting.
• Hootsuite — Helpful for monitoring social engagement and pulling together performance reporting across channels; sentiment usually comes through integrations.
• Talkwalker — Enterprise-focused social listening platform with sentiment analysis, trend detection, and reporting dashboards.
• MonkeyLearn or similar NLP tools — Better if you want to build a custom workflow: export comments → run sentiment/topic classification → merge with video metrics → analyze correlations.

A typical workflow looks like:

1. Export video metrics:

• Views over time
• Watch time
• Average retention
• Likes/dislikes
• Shares
• Subscriber changes
• Collect comments:

• Timestamped comments
• Likes/replies
• Comment volume by day/week
• Run sentiment analysis:

• Positive/neutral/negative scores
• Emerging topics
• Repeated complaints or praise
• Correlate:

• “Did positive sentiment spikes precede view growth?”
• “Do negative comments appear when retention drops?”
• “Which topics drive engagement?”

For a solo creator, a practical stack is often:

• YouTube Studio + exported comments + a spreadsheet/dashboard tool + an AI/NLP sentiment analyzer.

For a brand or media team managing many videos, Brandwatch, Talkwalker, or Sprout Social are usually more appropriate.

For that analysis, a data-analysis tool is the best fit—ideally one that can ingest video analytics plus comment text and calculate sentiment over time.

A useful workflow is:

• Sentiment analysis: classify comments as positive/neutral/negative and track the mix by day/week.
• Video performance: pull views, watch time, likes, shares, engagement rate, etc.
• Correlation analysis: compare sentiment changes with performance changes, including possible time lags (e.g., whether a sentiment spike precedes higher views).
• Visualization: use a line chart for trends and a scatter plot for sentiment vs. performance.

If your data is in YouTube, TikTok, Instagram, or a spreadsheet/CSV, tell me which one and I can recommend the most appropriate tool/workflow for it.

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

• Comment text → sentiment scores over time
• Video metrics → views, watch time, likes, shares, engagement rate, etc.
• Time alignment → daily/weekly/monthly trends
• Correlation analysis → whether changes in sentiment are associated with changes in performance
• Visualization → sentiment vs. views/engagement as time-series or scatter plots

If you mean which tool available in this ChatGPT session, I can help analyze the data if you upload/export the comments and video-performance data (CSV, Excel, etc.). I can then calculate sentiment trends, correlations, and visualize the relationships.

A useful setup would look like:

Date → Video ID → Comment → Sentiment → Views → Watch time → Likes → Shares

Then we can test questions like “Do videos with improving positive sentiment subsequently get more views?” rather than just comparing sentiment and performance at the same time.

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

• Comment-level sentiment (positive/negative/neutral or a sentiment score)
• Comment volume over time
• Video metrics such as views, watch time, likes, shares, and engagement rate
• Time-based correlation between sentiment changes and performance changes

If you're working with YouTube data, Python/pandas + a sentiment-analysis model is a particularly flexible approach. You can aggregate sentiment by day/week, join it to video performance metrics, and calculate correlations or lagged relationships.

A useful workflow is:

1. Extract comments and video metrics
2. Score each comment's sentiment
3. Aggregate sentiment and engagement by time period
4. Plot sentiment vs. views/watch time
5. Calculate correlations, including lagged correlations (e.g., whether a sentiment spike precedes a view spike)
6. Segment by video to identify which topics or sentiment patterns predict stronger performance

If you tell me where your comments and video metrics live (YouTube, TikTok, Instagram, CSV, etc.), I can point you to the specific tool/workflow that fits best.

If you mean a tool/workflow for this analysis, a good setup is:

• YouTube Analytics — video performance over time: views, watch time, engagement, CTR, etc.
• YouTube comments + a sentiment-analysis tool/model — classify comments as positive, neutral, or negative and track sentiment by date.
• Google Sheets, Excel, or a BI tool — join the sentiment data to video-level performance by date/video and calculate correlations.

The key analysis would be something like:

Comment sentiment → engagement/performance over time

For example, compare weekly average sentiment against views, watch time, likes, or subscriber growth, then use a scatter plot/correlation coefficient to test whether changes in sentiment are associated with performance.

If you're looking for one specific software tool that can ingest the comments and performance data and do most of this automatically, tell me what platform your videos are on (e.g. YouTube, TikTok, Instagram), and I can recommend the best current options.

For analyzing comment sentiment trends and how they correlate with video performance over time, you’d typically combine a few types of tools rather than use one single tool:

1. Video analytics platforms (performance data)

• YouTube Analytics — tracks views, watch time, retention, likes, comments, subscribers gained, traffic sources, and performance over time for YouTube videos.
• TikTok Analytics — provides TikTok content performance metrics (where available through creator/business accounts).
• Meta Business Suite — useful for Facebook/Instagram video performance metrics.

2. Sentiment analysis tools (comment analysis)

• Google Cloud Natural Language AI — analyzes sentiment, entities, and text categories at scale.
• Azure AI Language — provides sentiment scoring and opinion mining.
• Hugging Face Models — offers open-source sentiment models you can customize.

3. Data analysis / correlation tools

• Tableau or Microsoft Power BI — connect sentiment scores with metrics like views, engagement rate, retention, and subscriber growth to visualize trends.
• Python with libraries such as pandas, matplotlib, and NLP libraries — useful for custom analysis.

Typical workflow

1. Export comments from your video platform.
2. Run comments through a sentiment model:
   • positive / neutral / negative
   • sentiment score over time
   • recurring topics or complaints
3. Join sentiment data with video metrics:
   • views by day
   • average watch time
   • engagement rate
   • likes/dislikes
   • subscriber conversion
4. Analyze relationships:
   • Do positive comments predict higher retention?
   • Do negative spikes happen after certain content changes?
   • Which topics drive both comments and views?

For a no-code setup, a common stack is YouTube Analytics + sentiment analysis API + Power BI/Tableau dashboard. For a more flexible research workflow, YouTube API + Python + NLP models + visualization is usually the strongest approach.

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.