Extract categories in data

A tool that analyzes text data from a CSV file to identify and suggest main categories, themes, and topics.


The 'Extract categories in data' tool is a sophisticated solution designed by Scott Henderson for public use. It serves to streamline the process of categorizing large volumes of text data. By uploading a CSV file, users can quickly identify recurring themes and topics within their data set. The tool is flexible, allowing users to specify the column containing the text to be analyzed, select a range of rows for analysis, and set parameters for word count and taxonomy limits. It also accommodates a user-defined objective to tailor the categorization process to specific needs.

Use cases

The tool can be used by content managers to identify trending topics in customer feedback, by market researchers to categorize open-ended survey responses, or by data analysts to distill themes from large volumes of textual data for reporting and insights.


This tool offers a streamlined approach to data categorization, saving time and effort in manual analysis. It provides clarity by extracting relevant topics from large text datasets, aiding in content organization and strategy development. The tool's flexibility in parameters and objectives allows for customized analysis, making it a versatile asset for various data categorization needs.

How it works

Upon uploading a CSV file via a URL, the user specifies the target column with the text data. The tool then processes a selected range of rows, applying constraints such as word count per category and the number of categories to extract. It uses JavaScript code transformations to filter and prepare the data, which is then fed into an AI model. The AI model generates a list of topics based on the provided constraints and objectives. The output is a structured list of topics, neatly organized and ready for use.

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