Interpret prediction results from text sentiment analysis models

Starting on September 15, 2024, you can only customize classification, entity extraction, and sentiment analysis objectives by moving to Vertex AI Gemini prompts and tuning. Training or updating models for Vertex AI AutoML for Text classification, entity extraction, and sentiment analysis objectives will no longer be available. You can continue using existing Vertex AI AutoML Text models until June 15, 2025. For a comparison of AutoML text and Gemini, see Gemini for AutoML text users. For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see Introduction to tuning. To get started with tuning, see Model tuning for Gemini text models

After requesting a prediction, Vertex AI returns results based on your model's objective. Predictions from sentiment analysis models return the overall sentiment for a document. The sentiment is represented by an integer from 0 to the model's max sentiment score, which can be equal to or less than 10. The maximum sentiment value for a model is set during training. For example, if a model was trained on a dataset with a maximum sentiment score of 2, predicted sentiment scores can be 0 (negative), 1 (neutral), or 2 (positive).

Example batch prediction output

The following sample is the predicted result for a single document. Because the model's maximum sentiment score is 8, the predicted sentiment for this sample is clearly positive.

Note: The following JSON Lines example includes line breaks for readability. In your JSON Lines files, line breaks are included only after each each JSON object.

{
  "instance": {"content": "gs://bucket/text.txt", "mimeType": "text/plain"},
  "prediction": {"sentiment": 8}
}