AI Clustering

Your one-stop-shop to transform text data to insight

When dealing with free text data, there are various processing steps to transform raw text data to insight. The AI Clustering workflow comprises the most common and most beneficial processing steps (i.e. vectorizing, clustering, sentiment and emotion analysis). It takes care of all the required steps. So all you need to do is follow the setup wizard!


AI Clustering

This workflow categorizes your text data and presents the results on the Explorer app. It provides you with optional processing steps such as emotion and sentiment analysis. All you need to do is follow the setup wizard.

Note: Theme identification is of one-to-one grouping type.
Here, all entries in a dataset are processed. Themes are identified based on conceptual similarities. Each entry is assigned to only one of the themes. This is called clustering.
Sydney's weather and landscape is amazing => theme = Sydney

How to run AI Clustering

Once you have uploaded your data, select your dataset and click on "AI Clustering" under workflows.

Relevance AI - Access to AI-Clustering workflow

Relevance AI - Access to AI-Clustering workflow

This will open the setup wizard as shown in the image below:

Relevance AI - AI clustering setup

Relevance AI - AI clustering setup

On this page you will

  1. Select the free text field that you wish to analyse
  2. Enter the number of categories you wish to see
    Note 1: this number identifies to how many groups your data will be broken
    Note 2: smaller numbers result in high-level overview of the data, whereas larger values will break the data into more groups
    Note 3: if you have an overview of the data, knowing there are N categories (e.g. you know there are roughly 45 categories in a customer feedback dataset) you should enter N, otherwise we recommend 5% of the size of your dataset (i.e. number of entries in the dataset). Read our guid on How to select the number of clusters
    Note 4: Using "Add another variant", you can run multiple clustering analysis in parallel
  3. You can select to run three very beneficial processings steps alongside categorization:
    • Sentiment analysis: identify the polarity of the text data
    • Emotion analysis: identify the underlying emotion in the text date
    • Count character, word and sentence: add metrics on text statistics to your data
  4. Identify category field(s) to be used when presenting data on the Explorer dashboard. AI categorized data entries can be further grouped by fields such as gender, nationality, department, state, etc. This helps to better understand the data.
  5. Click on "Get started" to activate the workflow

Workflow progress is shown in a window similar to the image below.

When all steps are finalized, you will receive and email notification. "Continue to analysis dashboard" button will be activated and you will be directed to your preset Explorer dashboard. Read more about Explorer features and how to personalize it.

Relevance AI also provides you with one-to-many grouping in theme identification:
That is all entries in a dataset are processed. Tags/code frames are extracted. Each entry is tagged with relevant tags based on conceptual similarity. This scenario is called tagging.
Sydney's weather and landscape is amazing => `tags = Sydney, weather, landscape

See our guid on AI Tagging.