Analyze My First Dataset

Steps to analyse a text field with two herotText workflows

On this page, we explain two text analysis scenarios, both of which rely on state-of-the-art AI techniques.

  1. Identifying Themes: one-to-one grouping
    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 scenario is called clustering.
    Example:
    Sydney's weather and landscape is amazing => theme = Sydney

  2. Identifying Themes: one-to-many grouping
    Here, 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.
    Example:
    Sydney's weather and landscape is amazing => tags = Sydney, weather, landscape

Note: Following steps require zero coding/programming.

Clustering

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Clustering

Identifying themes: one-to-one grouping

The 2 main components for text clustering are shown in the image below. Each component is a no-code workflow. Step-by-step guide is available via the provided links.

Relevance AI - AI Clustering flow

Relevance AI - AI Clustering flow

1. Upload Data

Prepare your dataset as a CSV file and upload your data to Relevance AI's platform.

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Non-English Data? Not a problem :)

If your free-text data is not in English or is composed of multiple languages, make sure to use our no-code translation workflow after uploading your data.

2. AI Clustering

AI Clustering workflow groups your data entries based on their conceptual similarities and identifies themes. You will be presented with a pre-filled data Explorer dashboard. Explorer is your fully configurable dashboard to understand your data and extract insights. Have a glance at the Explorer many functionalities and follow our guides to create your Explorer dashboard.

Tagging

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Tagging

Identifying themes: one-to-many grouping

The 2 main components for Tagging are shown in the image below. Each component is a no-code workflow. Step-by-step guide is available via the provided links.

Relevance AI - AI Tagging flow

Relevance AI - AI Tagging flow

1. Upload Data

Prepare your dataset as a CSV file and upload your data to Relevance AI's platform.

👍

Non-English Data? Not a problem :)

If your free-text data is not in English or is composed of multiple languages, make sure to use our no-code translation workflow after uploading your data.

2. AI Tagging

Under Relevance AI's AI Tagging workflow, you will

  • use AI to automatically extract tags/code frames from your dataset
  • apply your knowledge of the field to modify (i.e. add to/remove from) the extracted tags
  • use AI to label your dataset
  • be presented with a pre-filled data Explorer dashboard. Explorer is your fully configurable dashboard to understand your data and extract insights. Have a glance at the Explorer many functionalities and follow our guides to create your Explorer dashboard.

Common Questions?

  • What if I have my own tags and don't need AI to generate the tags/code frames?
    You can skip step 2 and 3 in the Tagging scenario and follow the instruction at Tag With Your Code Frames instead.
  • How can I perform sentiment analysis?
    You can run Sentiment Analysis at any stage after your data is uploaded.
  • How can I perform emotion analysis?
    You can run Emotion Analysis at any stage after your data is uploaded.

Useful links

How to better prepare my data

Find insights through clustering

Find insights through tagging

The Explorer Dashboard

Types of analytics available at Relevance AI

How to work with my selected workflows