Yabble Count is a key feature of Yabble, our revolutionary AI-powered insights generator. Using a combination of cutting-edge and proprietary algorithms and technology, it takes any unstructured text data and analyzes it for themes, sub-themes, and sentiment.

The insights that Count provides enable you to immediately identify the key things you can do to drive business growth. They also highlight opportunities for summarization queries, guiding you to areas you can mine for even deeper insights.

Count helps you pinpoint places for improvement, allows you to optimize your customer and brand experiences, and validates business strategy and decisions.

Yabble Count is capable of analyzing data from nearly any source, including survey responses, product reviews, and unnested comments from social media. Future releases will include more advanced social media data and call center data.

Eg. if I only have demographic data for some of my respondents, will it still upload successfully?

Yes, you can in some cases, but it is not recommended. There are some fields that are required for the import to work – please see the ‘Preparing your data for import’ section in Intercom to find out more. If some responses don’t have an answer, the data will still be processed, but it may impact your ability to filter responses accurately.

Theme and sub-theme counts allow you to unlock immense value from your text data. They enable you to see the most talked-about subjects within a particular dataset and can tell you instantly where the biggest drivers and inhibitors of your business lie.

Themes are one- to two-word subjects that highlight the main areas of response within your dataset. These are high-level buckets such as management, customer service, online shopping, and shipping.

Sub-themes add layers of rich detail to your themes. For each theme that Count generates from your dataset, you’ll see all the sub-themes that sit underneath it, be they positive, neutral, or negative. For example:

Sub-themes are one of the true goldmines of Count and allow you to:

Sentiment gives you an indication of how people feel about a particular subject. Yabble has two distinct level of sentiment, an overall sentiment snapshot and a detailed comment level sentiment which layers into themes and sub themes.

Overall sentiment indicates the sentiment breakdown within your entire dataset at a glance. Overall sentiment is an aggregate score comprised of the sentiment score of each comment, averaged. It’s displayed as a donut chart at the top of your Count project’s Explore page, identifying the percentage contribution of Negatives, Neutrals, and Positives to your Overall Sentiment.
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How is overall sentiment and comment sentiment calculated?

The sentiment score for overall sentiment and comment sentiment is based on a scale of -100 to 100 and is calculated using Google algorithms. The thresholds are:

This gives you an immediate numerical snapshot of the positive, negative, and neutral feelings around a particular subject. You can view sentiment on an individual comment level, indicating at a glance whether a particular comment is positive, neutral, or negative.

Yabble calculates sentiment for each individual theme and sub-theme, displaying sub-theme sentiment in the form of an emoticon. Positive is green, neutral is orange, and negative is red. (For instance: you would see a red emoticon next to the Parking > Small spaces sub-theme.) If you hover over the emoticon, you’ll see a detailed count and percentage breakdown of the positive, neutral, and negative buckets.
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For example

This comment has generated three themes and sub-themes, each with their own sentiment. Sentiment is displayed in the form of an emoticon. Positive is green, neutral is orange, and negative is red. (For instance: you would see a red emoticon next to the Checkout > No cashier service available sub-theme). If you hover over the emoticon, you’ll see a detailed count and percentage breakdown of the positive, neutral, and negative buckets.

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The filter functionality allows you to filter by different variables and to focus on certain segments of your results. You can filter by:

Future releases will include the ability to filter by sub-themes as well.

Your Explore page displays a clear, concise visualization of the analysis Count has performed on your dataset.

Your overall sentiment score sits at the top, next to a box linking you to Gen, enabling you to run a start a chat with your data straight from your counting results.

Below your sentiment score, you’ll see your ‘Theme overview’ with a table of the themes and nested sub-themes. You’ll also see the Comments/Filters sidebar to the right.

Themes are displayed at the top level from your dataset, alongside percentage and sentiment scores for each. The percentage indicates the frequency with which that theme arose during analysis.

You can toggle this percentage to be '% of total' or '% of theme'.

'% of total' is the percentage of the comments that make up this theme or sub-theme that contribute to the total number of comments analyzed. For example, consider the following scenario:

'Availability' is 50% of the total, 'Delivery' is 30% of the total, and 'Accuracy' is 20% of the total.

'% of theme' is particularly relevant for sub-themes, and shows the percentage of comments in a sub-theme compared with the total comments in a theme. For example, consider the following scenario:

'All items in stock' is 60% of the theme and 30% of the total.

'Online unavailability' is 30% of the theme and 15% of the total.

'Out of stock items' is 10% of the theme and 5% of the total.

If you click the arrow (>) next to a theme, your sub-themes will populate underneath, alongside their own percentage counts and sentiment (displayed as emoticons).

If you select ‘Sub-themes’ from the drop-down, you will see all your sub-themes in one list. They’re ordered from highest to lowest count, irrespective of their parent themes — but you can see the corresponding theme listed next to each in parentheses (i.e. Spacious parking spots (Parking)). Note: the order of sub-themes in the overall Sub-themes tab may differ from the order of sub-themes under each parent theme.

Hovering over the emoticon next to a sub-theme will generate that sub-theme’s individual sentiment score (counts and percentages for positive, neutral, and negative).

Count enables you to identify the areas of your dataset that are worth diving deeper into. You can then chat with your data using Gen, your ultimate AI Research Assistant, to uncover more insights in minutes.

Definitely! Yabble Count allows for data export via CSV and SPSS.

Count uses Yabble credits as currency. One comment = one credit. So for instance: if you run Count on 500 comments, you’ll spend 500 credits.

If your upload fails and you get an error message, double-check the below:

You can check your failure reasons by clicking into your project, click 'Failed' and then click the eye in the pop-up modal. This will tell you why your file has failed and show affected rows that need to be corrected before you can re-import.

The minimum timeframe for a trend summary is one week. Outside of that, you can adjust the display to any period you’d like, whether that’s month-to-month, quarter-to-quarter, year-to-year, or a custom segment of your choosing.

To change the display summary, click the Week tab and disable the time periods you don’t want to see.

The ratio of positive to negative sentiment scores will shift as you adjust the timeframe of your trend summary.

Trend summary sentiment scores are measured based on the change from the previous time period.

The plotted points you’ll see on your trend summary graph reflect the percentage of positive comments within the timeframe you’ve selected. They’ll change when you update the timeframe.