Determining the emotional tone of text
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.
- Ensure that the system locale language settings are set to UTF-8.
- Specify a repository for text analytics models. For more information, see Specifying a database for Prediction Studio records.
- Setting up a sentiment analysis model
Start the build process of a sentiment analysis model in Prediction Studio by selecting the model type and the language of the model that you want to build.
- Preparing data for sentiment analysis
In the Lexicon selection step, select the sentiment lexicon to use for sentiment analysis. Sentiment lexicons contain features that are used to enhance the accuracy of the model.
- Uploading data for training and testing of the sentiment analysis model
In the Source selection step, select the source for training and testing data that is required to create a model.
- Defining the training set and training the sentiment analysis model
In the Sample construction step, split the data into the set that is used to train the model and the set that is used to test the model's accuracy.
- Reviewing the sentiment analysis model
When a model is created, analyze its accuracy in the Model analysis step.
- Saving the sentiment analysis model
In the Model selection step, export the file with the model or save the model as a Decision Data rule to use that model as part of the Pega Platform text analytics feature.
- 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.
- 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.
- Testing text analytics models
You can perform ad-hoc testing of text analytics models that you created and analyze their performance in real-time, on real-life data.