Through intent analysis, you can determine the expressed intent of your customers or product reviewers.
For example, you can detect whether a specific user likes or dislikes your product, wants to complain, or asks a question about product's features. Intent detection helps you properly triage user comments and queries to quickly and efficiently address any potential issues. See Default intent model for an overview of the default intent detection model that can help you understand intent analysis and provide a starting point for developing custom intent detection models that best fit your business objectives.
Intent analysis can produce insightful results when it is combined with other analysis types of analysis in your application. For example, consider the message: My uPlusPhone-01 touch screen has suddenly stopped responding! Very unhappy. I am going to return it and demand a refund. Switching over to competition.
By combining the default pzDefaultIntentModel intent detection model with sentiment and text extraction analysis types, you can derive the following information automatically:
- Entities – My uPlusPhone-01 touch screen. This is the value of py.Entities(1).pyName property of type auto_tags.
- Intents – Quit. This is the value of the pyIntents(1).pyName property that the text analyzer detected by applying the default pzDefaultIntentModel intent detection model.
- Sentiment – Negative. This is the value of the pyOverallSentiment property that holds the total calculated sentiment value of the analyzed document. The sentiment was derived by applying the default pySentimentModels model on the document.
This information might lead to triaging and taking remedial actions to retain a customer who is likely to quit the company's services.
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