Analyzing natural language
Effortlessly analyze and extract meaningful information from large volumes of text with the use of text analytics. Based on your findings, you can further improve business performance and customer experience.
With text analytics you can analyze and structure text, in multiple languages, that comes in through various channels, such as emails, social media platforms, and chat channels.
- Language support for NLP
Pega Platform provides text analytics based on natural language processing (NLP) that you can use to detect, process, and structure text data from email, chatbots, and social media platforms. Depending on the language of the analyzed content, various text analytics features help you obtain accurate analysis results.
- Labeling text with categories
Efficiently analyze large volumes of text and assign each sentence to a predefined category by using the text categorization feature. With text categorization, you can quickly react to what your customers are saying and aptly address their inquiries or concerns.
- Building text analyzers
Text analyzer rule provides sentiment, categorization, text extraction, and intent analysis of text-based content such as news feeds, emails, and postings on social media streams including Facebook, and YouTube.
- Building text extraction models
Use to create text extraction models for text analytics. With text extraction, you can detect named entities from text data and assign them to predefined categories, such as names of organizations, locations, people, quantities, or values.
- Managing text analytics models
Data scientists can perform various housekeeping activities for sentiment and text classification models in the Predictions work area in Prediction Studio. The range of available activities depends on whether the model has been built (the displayed model status is Completed) or is incomplete (the displayed model status is In build).
- Sentiment lexicons
A sentiment lexicon is a list of semantic features for words and phrases. Use lexicons for creating machine learning-based sentiment and intent analysis models.
- Text analytics accuracy measures
Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.