Creating text categorization models
Use to create categorization models for text analytics.
Text categorization models assign incoming text to a predefined category, for example,
sentiment type or a topic.
Creating machine-learning topic detection models
Topic detection models classify text into one of several categories. You can use this type of analysis in customer service to automatically classify customer queries into categories, thus increasing the response time. By classifying text, you can also route the query directly to the right agent.
Creating keyword-based topic detection models
You can create a topic detection model that analyzes a piece of text by checking whether it contains any topic-specific keywords. If that model encounters any topic-specific keywords in the analyzed text, the model assigns that piece of text to the corresponding topic. Keyword-based categorization models act as substitutes or supplements for machine learning categorization models in cases in which machine learning models are not fully developed or do not produce satisfactory results, for example, when they have low prediction accuracy.
Configuring sentiment analysis
Sentiment analysis determines whether the opinion that the writer expressed in a piece of text is positive, neutral, or negative. Knowledge about customers' sentiments can be very important because customers often share their opinions, reactions, and attitudes toward products and services in social media or communicate directly through chat channels.
Configuring intent detection
Create intent analysis models to enable your application to detect the ideas that users express through written communication. For example, you can use an intent model when you want your chatbot to understand and respond when someone asks for help.