Interpret prediction results from text sentiment analysis models
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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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[],[],null,["# Interpret prediction results from text sentiment analysis models\n\n| 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](/vertex-ai/docs/start/automl-gemini-comparison). For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see [Introduction to tuning](/vertex-ai/generative-ai/docs/models/tune-gemini-overview). To get started with tuning, see [Model tuning for Gemini text models](/vertex-ai/generative-ai/docs/models/tune_gemini/tune-gemini-learn)\n\nAfter requesting a prediction, Vertex AI returns results based on your\nmodel's objective. Predictions from sentiment analysis models return the overall\nsentiment for a document. The sentiment is represented by an integer from 0 to\nthe model's max sentiment score, which can be equal to or less than 10. The\nmaximum sentiment value for a model is set during training. For example, if a\nmodel was trained on a dataset with a maximum sentiment score of 2, predicted\nsentiment scores can be 0 (negative), 1 (neutral), or 2 (positive).\n\nExample batch prediction output\n-------------------------------\n\nThe following sample is the predicted result for a single document. Because the\nmodel's maximum sentiment score is 8, the predicted sentiment for this sample is\nclearly positive.\n\n\n| **Note**: The following JSON Lines example includes line breaks for\n| readability. In your JSON Lines files, line breaks are included only after each\n| each JSON object.\n\n\u003cbr /\u003e\n\n\n```\n{\n \"instance\": {\"content\": \"gs://bucket/text.txt\", \"mimeType\": \"text/plain\"},\n \"prediction\": {\"sentiment\": 8}\n}\n```"]]