How to Classify Text Data – Displayr Help

In any sort of automation procedure, there is a trade-off between speed and quality. Classifying text is no different, but Displayr has engineered a way of classification that gets you the best of both worlds.

You can use Displayr AI technology to assist in your text classification for a robust automatic categorization tool. See Displayr AI for proven examples and instructions on how to accept the terms and enable this feature. If you prefer not to use Displayr AI, our Displayr proprietary algorithm maps language to find similar responses in a "smarter" way than merely searching for exact keywords. This helps you find what you are looking for faster, while at the same time manually controlling how things are being classified.

This article is broken into the following sections:

Create Your Classification
Saving Your Classifications
Use Sort By to Assist With Classification
- Fuzzy match
- Similarity to
Reusing Existing Categorization
Create Additional Themes

In this article, we describe how to go from raw text data:

To a state where the text responses have been classified and can be used for further analysis:

Requirements

You will need a Text variable. Text variables are represented by an A next to the variable label in the Data Sources tree:

Create Your Classification

From the Data Sources tree, select the text variable that you would like to classify.
Hover and click + > Text Categorization.
If you want to classify responses into multiple themes, select Multiple themes, or
If you want to classify responses into a single theme, then select Only one theme.
Click Start.

If you're not sure which option is best for the text data you're working with, see Types of Categorization for examples.

The Text Categorization module will open. You have the original text responses on the right:

Create Themes and Classify Text Responses

The Create feature in Displayr's text categorization tool creates a specified number of themes, and the Classify feature classifies responses into those themes.

Determine the number of themes you would like to initially create. The default is to create 10 themes.
Click Create.
Once the themes are created, you will see them in the Themes pane on the left side of the screen.
Click Classify to automatically assign the raw text responses to the created Themes.

When the classification is finished, you will see a colored number next to each text response. This corresponds with the theme it was assigned to.

No automatic classification solution will be perfect and there are ways that you can tidy up and improve your classifications so read on.

Resolve Unclassified Data

Once you've created themes and clicked classify, you may have some unclassified data that couldn't be assigned to a specific theme. You will know if any data is left unclassified if there is a non-zero number in the "Uncategorized" theme:

You will want to review any unclassified responses and classify them into an existing theme or create a new one. You can create a new theme, such as "All other responses", to capture the responses that don't fit into existing themes.

Set the Show responses from dropdown to Uncategorized to show all unclassified responses.
Right-click anywhere in the Themes pane and select Add Theme.
Give the new theme a label, e.g. "All other responses" and click OK.
Select the unclassified responses you want to classify.
In the Themes pane, select "All other responses" and then click Classify as.

Check the Quality of Classified Responses

It's important to give any automatic classification a quality check before reporting on your analysis. A quick way of doing that is to select a theme from the Show responses from dropdown, and quickly scan through.

If there are more than a few items that you need to reclassify into an entirely different theme, assign them all to the "Uncategorized" theme. Then click Classify again to re-run the AI theme assignments. This will only classify the unclassified items, it won't reclassify things that are already assigned to a theme.

Below, I've selected the "Brand Preference" theme from Show responses from and can see that there are some items, such as "People who drink Pepsi Light prefer the less chemically taste", that is better placed in another theme.

To manually reclassify responses:

Select the text response.
Then select the theme(s) to classify it as.
OPTIONAL: If moving to a different theme, deselect the theme(s) you want to move the text response from.
Click Move to, Add to, or Remove from.

Move to will remove the response from its current theme(s) and place it in the selected theme(s). Add to will add it to the selected theme(s). Remove from will remove responses from the selected theme(s). Below, "People who drink Pepsi Light prefer the less chemically taste" is moved to "Taste Preference" and removed from "Brand Preference".

You can delete existing themes and reclassify them into an existing theme by right-clicking and selecting Delete and Reclassify as (and then selecting the theme to classify into), or delete a theme altogether by right-clicking and selecting Delete.

Alternatively, you can add more themes manually, or run the Create function again to create additional themes using AI. See the Create Additional Themes section below for more information.

See How to Refine and Edit Text Themes After Classification for details and examples for refining and editing your text themes.

Saving Your Classifications

Once you're happy with the classification, click Save in the bottom right corner. This will take you back to Displayr's main Edit mode interface. A new classified variable set will appear in the Data Sources tree next to your original text variable and have "Categorized" in the name.

It will have an icon with two radio buttons (Nominal structure) if you selected Only one theme or an icon with two boxes (Binary - Multi structure) if you selected Multiple themes when you started the process. You can read more about Displayr's variable set structures here.

Use Sort By to Assist With Classification

You can use our Sort by algorithms to help you manually classify remaining text data into existing themes.

Fuzzy match

Fuzzy match sorting uses keywords to find responses that are similar in the unclassified data.

In the text categorization interface, update the Sort by dropdown to Fuzzy match.
Enter a keyword or phrase that you want to find similar matches for in the Fuzzy sort on field.
Click Sort now.

The responses will update to show an orange bar to the left of the text. The length of the bar indicates the match level, so the longer the bar, the better the match.

In the example below, I have some text data that contains responses to what people miss about pre-pandemic life. I used the Fuzzy match algorithm to find responses that are similar to "travel":

To assign the fuzzy match results to an existing theme:

Select the response(s) from the pane on the right that you want to classify.
Select the theme on the left to classify the response.
Optional: If you selected Multiple themes when at the start of classification, click Classify as to assign the selected items to a theme.

Next, I can scan through the results and identify responses to classify into my existing "Reduced Travel" theme.

Similarity to

The Similarity to algorithm will look at responses that have been classified to a specific theme, and then use those to find similar responses in the unclassified data. The algorithm becomes smarter as more responses are classified to a theme.

Update the Sort by dropdown to Similarity to... (selecting the existing theme that you want to find similar matches for).
The responses will update to show an orange bar to the left of the text. The length of the bar indicates the match level, so the longer the bar, the better the match.
Select the desired response(s) from the panel on the right.
Select the theme on the left to classify the response.
Optional: if you selected Multiple themes at the start of classification, click Classify as to assign the selected responses to the selected theme(s).

Reusing Existing Categorization

If you want to use existing themes from an already classified variable on an unclassified text variable and still be able to make use of features available in Displayr's categorization tool, you can reuse existing categorization.

For example, I have a text variable that asked about how social life has changed due to the pandemic that I classified. Next, I want to apply the same themes to another text variable that asked about what parts of social life from pre-pandemic are missed. I can reuse the existing categorization on this other text variable.

Select the text variable that you want to classify using existing themes.
Hover and click + > Text Categorization.
Click the Reuse Existing Categorization tab.
Select the variable that contains the existing themes that you want to use.
Click one of the following options:
- Reuse by duplicating - copies an existing code frame and rules as is from a categorized variable set.
- Reuse by sharing - shares an existing code frame and rules from a categorized variable set, so that any additions and changes will be reflected in both.
The text responses will appear on the right side, and the existing themes appear in the Themes pane to the left. To assign the text responses to an existing theme, click Classify.
Repeat the steps in the Resolve Unclassified Data and Check the Quality of Classified Responses sections above as needed.
Click Save when you are finished with classification.

Create Additional Themes

There may be instances when you have existing themes but want the create additional themes based on unclassified data. Using the Create function helps with this. It can be useful if you've updated your data with new responses or if you have a lot of leftover unclassified data.

With the text categorization tool open, adjust the number field next to Create to indicate how many new themes you'd like to add.
New themes will be added to the bottom of the existing list.
Click Classify to assign unclassified data to any of the existing themes.
Repeat the steps in the Resolve Unclassified Data and Check the Quality of Classified Responses sections above as needed.
Click Save when you are finished with classification.