Welcome to AutoML Natural Language Sentiment Analysis, which enables you to create custom machine learning models to analyze attitudes within English-language text.
Custom machine learning models are useful when the sentiments that are available from the Natural Language API are too generic or not applicable to your specific use case or knowledge domain.
In this section
- Quickstart - Use the AutoML Natural Language Sentiment Analysis UI to train and validate a custom model for classifying content.
- Before you begin - Information on setting up a Google Cloud Platform project that you can use with AutoML Natural Language Sentiment Analysis.
- Preparing your training data - Information on preparing and formatting training data for AutoML Natural Language Sentiment Analysis.
- Creating datasets, training models, and evaluating results - How to complete the main steps in creating your custom model.
- REST Reference
- RPC Reference
You can view discussions and post questions and feedback to the Natural Language discussion group.