This type of text analysis determines the topics to which a text unit should be assigned. In Pega Platform, topic detection is achieved by means of machine learning-based and keyword-based models. By categorizing text into topics, you can make it easier to manage and sort, for example, you can group related queries in customer support.
A keyword-based model is a list of semantic categories that are related to a particular domain. The semantic categories are grouped in taxonomies and have hierarchical relationships, for example: Safety concerns, "theft, steal, break, rob, intruder".
Some taxonomies are provided by default in the
.csv format. You can create custom taxonomies that suit your business needs. For more information, see the article Requirements and best practices for creating a taxonomy for rule-based classification analysis on the Pega Community.
Machine-learning models for topic detection
Pega Platform uses maximum entropy, Naive Bayes, and support vector machine algorithms to train topic detection models. Machine learning-based topic detection can help businesses improve the effectiveness of their customer support services. By classifying customer queries into topics, the relevant information can be accessed more quickly, which increases the speed of customer support response times. You can train custom categorization analysis models in the Prediction Studio overview.
- Configuring topic detection
Detect topics (talking points) of the text to automatically classify user queries and shorten customer service response times.
- Creating machine-learning topic detection models
Efficiently connect your customers with the right consultant by providing training data to a topic detection model.
- Creating keyword-based topic detection models
Efficiently connect your customers with the right consultant without having to provide training data to the topic detection model. Instead, you can use a list of topic-specific keywords to train the model.