> For the complete documentation index, see [llms.txt](https://yourcomments.gitbook.io/home/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://yourcomments.gitbook.io/home/per-video-analysis.md).

# Per Video Analysis

"Per Video" results are for a given period in time.  There is no aggregate or cumulative analysis.

## Main Tab

On this tab, you will find a quick glance at the sentiment and distribution from a more zoomed-out view.

### Sentiment Gauge

The Sentiment Gauge is the average sentiment across all [first-order comments](/home/terminology.md#first-order-comments).

<figure><img src="/files/3Xg8TJN5kWCDzQgtq1VJ" alt="" width="375"><figcaption></figcaption></figure>

Use this chart for the most intense of `tl;dr` possible on the platform.

### Sentiment Bar Chart

Bins for each sentiment displaying the count of [first-order comments](/home/terminology.md#first-order-comments).

<figure><img src="/files/ZTE8aZrZqbAZYHJHCWyj" alt="" width="375"><figcaption></figcaption></figure>

An easy-to-look-at, fast-to-grok, spread of comment sentiment for an asset, such as a YouTube video.&#x20;

### Sentiment Over Time (SOT)

A linear representation of comments over time.

Because this dataset can be 1 hour old or 15 years old, a scale of `0` to `100` is used. The x-axis numbers are the `%` number of time passed for the data provided.&#x20;

<figure><img src="/files/kJtgCf9opvva8wiwOpqZ" alt="" width="375"><figcaption></figcaption></figure>

The SOT view is keen to show comment trends over time. &#x20;

* **Were the comments strong on release?**
  * Early positive comments indicate a loyal audience eager to see new content and engaged.
  * Early negative comments can drive down viewership and require the channel owner to devote more attention.
  * Heavy comments in general can indicate that the algorithm is helping or that it's a loyal fan base.
* **What does the distribution of comments look like?**
  * Early burst, explained above
  * Middle burst via sharing, legacy media (they usually lag), weekly or monthly reporting on a topic that you were included in.
  * Steady over time means content is getting shared, rewatched, or referenced enough to keep engagement up.
    * Positive comments are a sign of happy, thankful people
    * Negative comments require more investigation, such as political, social, or other "tribal" topics, rather than "your video actually sucks."
* **Do any visible bursts of comments correlate with other events?**
  * Is your marketing team doing something?
  * Did you do an interview, release something, say something that gained heat, or otherwise kick up dust?
  * **Is the burst abnormal, or is there a natural ebb and flow to comments?**
    * If the comment swells are not abnormal, look more closely at the timeline and learn how to increase the positive, healthy elements.
* **Is there a (positive or negative) coordinated activity?**
  * Numbers, in particular ratios, are important.  A radical shift in the comment ratio, especially over time, is an important signal of a possible mistake, an environmental shift, or an algorithmic change that matters.
  * Let's not lie, it's brutal online. You have to deal with all sorts of small to serious coordinated attacks.  This chart easily shows swells in comment behavior to examine further.
* **Virality, low traction, or steady comments**
  * Getting "heat" on your work will affect this graph. This can indicate the type of heat (good/bad)
  * Virality will usually swell and sustain.&#x20;
  * Low traction may look like an early burst and rapid dropoff.

## Comment List Tab

The Comment List tab is a sortable table of results. Included is a link (if possible) to the comment for the respective network or source.

<figure><img src="/files/jBcZk5v2O0ss9epECAtL" alt="" width="375"><figcaption></figcaption></figure>

Sort and search, this is your data.

### Global Scatter Plot Tab

Scatter plots are very cool,  and this one is no exception.  Visualize all comments over the data timeframe, with clickable links to the comment (when available) and the comment text.

<figure><img src="/files/TYaTNzYS7Nnmh4syaIfI" alt="" width="375"><figcaption></figcaption></figure>

Below is the Selected Comment Detail card showing you the comment for the selected plot (dot).  By selecting dots on the scatter plot, you can quickly dive into segments of comments, including outliers and groups.

### Like & Comment Drill Tab

#### Scatter: Comment > n

Displays the comments where the like count for an asset is greater than `n`  (filter).&#x20;

* `n` is the minimum like count filter
* Click on the graph to read the comment and a link to the comment

<figure><img src="/files/gJ5ro7geRX5MmMOKdY39" alt="" width="375"><figcaption></figcaption></figure>

Use this scatter plot to investigate, understand, and take action on well-liked (upvoted, hearted, etc) content. &#x20;

Comments with higher like counts can represent important voices of your audience or underscore positive and negative actions.

By filtering on the like count, you can understand not only what resonates with your audience but also the strength of the theme.

#### Scatter: Like count > n

This scatter plot is nearly identical to the Scatter: Comment > n graph, except that results are filtered by the count of comments to the first-order comment.

* `n` is the minimum comment count filter
* Click on the graph to read the comment and a link to the comment

<figure><img src="/files/aW2tRIWEBQpmxVtOGBPB" alt="" width="375"><figcaption></figcaption></figure>

[Second order+](/home/terminology.md#nth-order-comments) comments represent the time people take to not only read a comment, but respond to it or the other comments.  The why and who can show deep conversations, healthy debate, or something equivalent to the devil's taint on a hot August afternoon.


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