Text classification

Use the text classification model to automatically categorize text into groups.

You can use this model to parses the text, automatically assigning them to the groups (also called tags) that you defined. The algorithm looks at the name of each group and tries to assign words based on similarity, synonyms, and meaning. However, the algorithm cannot be used to capture the intent of an entire sentence.

Building a text classification model is increasingly important for natural language processing applications, as it provides a solid foundation for other machine learning applications.

You can use a text classifier to aid in decision making. By categorizing the text you can define the next step to be taken, that is, based on the words used it is possible to define which action to take.

Use IBM RPA tools to build a text classification model, train, and use in your applications.

• Planning for classifying text
  Learn how text classification works in IBM RPA and what you need to do before using it.

• Text classification algorithms
  Use the text classification models to classify a set of words from a resource based on three text classification algorithms: Bag-of-Words, N-Gram, and Text Classifier. Each of these algorithms has a functional model to do a specific treatment on the text documents.

• Training a text classification model
  Learn how to train text classification models.

• Using a text classification model
  Learn how to use your text classification model.