Analyze My Dataset via Individual Workflows/Transformations

Hero text workflows broken down to their components

On the Analyze my first dataset page, we explained two major workflows that are used for two very common text analysis scenarios, AI tagging and AI Clustering. These workflows are composed of a few smaller workflows which are executed under the hood. On this page, we want to show how one can run multiple smaller workflows on a dataset based on their required processing.

The list of available workflows on Relevance AI can help you get to know:

  • the no-code processing tools (workflows) available on Relevance AI's platform
  • their common use cases and benefits
  • how to run them

Following this page, we will explain clustering and tagging components. Note that this page's goal is to point out how one can execute a set of selected workflows not how to Cluster/tag data. Guides specifically written on text clustering and tagging are available at Ai Clustering and AI tagging.

Clustering steps

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Clustering

Identifying themes: one-to-one grouping

The 4 main components for 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 - Cluster flowRelevance AI - Cluster flow

Relevance AI - Cluster 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. Vectorize

Vectors are representation of your data in a language that AI understand. Turn your data into vectors using the vectorize workflow.

3. Cluster

Clustering groups the data based on their conceptual similarities and identifies themes. Group your data using the Cluster workflow.

4.Explorer

Explorer is your fully configurable dashboard to understand your data and extract insights. Have a glance at the Explorer's many functionalities and follow our guides to create your Explorer dashboard.

Tagging steps

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Tagging

Identifying themes: one-to-many grouping

The 4 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 - Tagging flowRelevance AI - Tagging flow

Relevance 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. Generate AI Tags

Use AI to automatically extract the most common tags/code frames from your dataset and label your data through the Generate AI tags workflow.

3. Guided Tagging

Apply your knowledge of the field to modify (i.e. add to/remove from) the extracted tag list, then label your data through Guided tagging.

4.Explorer

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.

Useful links

How to split my text data to its composing sentences

How to upload media files (e.g. image, audio)

How to perform sentiment analysis

How to perform emotion analysis