![How to collect free-text feedback: an introduction for a data scientist How to collect free-text feedback: an introduction for a data scientist](https://i3.wp.com/miro.medium.com/v2/resize:fit:875/1*Yx8UYyy6b6TTDLZW9s0Ftw.png?w=875&resize=875,599&ssl=1)
How to collect free-text feedback: an introduction for a data scientist
Last Updated on July 18, 2023 by Editorial Team
Author(s): Anil Tilbe
Originally published on Towards AI.
Understand how to develop technical learning systems to collect free-text, open-ended responses from users.
Photo by Emily Morter from Unsplash
To truly understand the type of measurement framework to implement for how to solicit feedback is to also humbly acknowledge as a data scientist the shortcomings and imprecise capabilities of natural language processing and machine learning.
Control +F the number of times I mentioned “primary source.”
Use case: analyze free-text comments; predict their binary sentiment (positive or negative); and measure the magnitude of that sentiment (e.g., polarities in TextBlob; the positive or compound score in VADER; your custom sentiment score for your custom-trained model;… Read the full blog for free on Medium.
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Published via Towards AI