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Best practices for providing feedback for text categorization models

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When working with text categorization models (sentiment, topic, and intent detection), you must select a portion of the text and provide your feedback.

Consider the following sample classification use case:

  • Text to analyze – I bought this new phone from UplusTelco. It is an amazing phone with a great camera. The battery life is excellent. However, I had some issues with the warranty period time limit. I decided to call customer care. They put me on hold for 3 hours and were very unhelpful. Finally, they told me the additional warranty offer has expired last year.
  • Taxonomy topics – Customer Service, Warranty, Phone Camera, Screen Brightness
  • Topic expected – Customer Service
  • Topic found – Warranty

Analysis

The talking points in the analyzed document include the phone camera, warranty, and customer service. Therefore, providing feedback on the entire document is inefficient and can confuse the model, which is detrimental to the classification accuracy.

Solution

Be as granular as possible when providing feedback for text categorization. Divide the analyzed document and provide excerpt-specific feedback. For example:

  • Document to analyze – I decided to call customer care. They put me on hold for 3 hours and were very unhelpful.
  • ActualResult – Customer service

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