Data that pops: Keatext’s new statistics module illustrates every insight

Keatext’s statistics module allows users to create charts based on data points, patterns, and insights derived from NLP sentiment analysis.

Keatext’s statistics module allows users to stay in the Keatext platform to create their own charts based on key data points, patterns in the data, and the insights they want to highlight.

The process begins with a dataset of any size, uploaded by a Keatext user and analyzed by the platform’s AI data analysis tool. Keatext’s statistics module works on the principle of pivot tables, allowing users to configure the layout of the data findings in several ways, such as seeing the selected fields as rows in a hierarchical column, or displaying the results as a pivot view where the selected fields are in separate rows and columns with totals at the end of each row.

Making visual sense of data hierarchies with NLP sentiment analysis

With data laid out on the screen, the user can then access the tool’s Fields popup, where they can select the data they want to show in a grid or chart by dragging and dropping hierarchies from the Fields list to the Rows, Columns, Values, or Report Filters boxes.

This means that Keatext users can focus on the data that interests them most for a given purpose, such as particular details about who the data is based on, including location and age, or, considering Keatext’s focus on analyzing people’s verbatim feedback, certain emotion-based negative and positive sentiments within customer or employee feedback data. Cells from the data source can also be formatted for number formatting and conditional formatting.

Keatext’s pivot charts let users visualize data in a new way by using filters to drill down through data to better choose what to highlight in a graph or grid. As part of a platform created to reveal insights within data, this visualization tool enables users segment their data by field so that any viewer can understand that data in relation to the other fields it exists within—thereby rendering insights with more clarity. For example, the statistics module can produce a colour-coded chart that lets viewers compare how many people bought an item in several different cities and how their satisfaction levels might differ.

The colours of the visuals can be changed to a company’s brand colours and every chart or grid is easy to export into multiple formats.

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