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What is sentiment analysis and opinion mining in the Language service?
Azure AI services
An overview of the sentiment analysis feature in Azure AI services, which helps you find out what people think of a topic by mining text for clues.
jboback
nitinme
azure-ai-language
overview
01/25/2024
jboback
language-service-sentiment-opinion-mining

What is sentiment analysis and opinion mining?

Sentiment analysis and opinion mining are features offered by the Language service, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. These features help you find out what people think of your brand or topic by mining text for clues about positive or negative sentiment, and can associate them with specific aspects of the text.

Both sentiment analysis and opinion mining work with a variety of written languages.

Sentiment analysis

The sentiment analysis feature provides sentiment labels (such as "negative", "neutral" and "positive") based on the highest confidence score found by the service at a sentence and document-level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral and negative sentiment.

Opinion mining

Opinion mining is a feature of sentiment analysis. Also known as aspect-based sentiment analysis in Natural Language Processing (NLP), this feature provides more granular information about the opinions related to words (such as the attributes of products or services) in text.

Prebuilt model

[!INCLUDE Typical workflow for pre-configured language features]

Get started with sentiment analysis

[!INCLUDE development options]

[!INCLUDE Developer reference]

Custom model (preview)

Custom sentiment analysis enables users to build custom AI models to classify text into sentiments pre-defined by the user. By creating a Custom sentiment analysis project, developers can iteratively label data, train, evaluate, and improve model performance before making it available for consumption. The quality of the labeled data greatly impacts model performance. To simplify building and customizing your model, the service offers a custom web portal that can be accessed through the Language studio. You can easily get started with the service by following the steps in this quickstart.

Project development lifecycle

Creating a Custom sentiment analysis project typically involves several different steps.

:::image type="content" source="media/development-lifecycle.png" alt-text="Diagram of the development lifecycle" lightbox="media/development-lifecycle.png":::

Follow these steps to get the most out of your model:

  1. Define your schema: Know your data and identify the sentiments you want, to avoid ambiguity.

  2. Label your data: The quality of data labeling is a key factor in determining model performance. Avoid ambiguity, make sure that your sentiments are clearly separable from each other.

  3. Train the model: Your model starts learning from your labeled data.

  4. View the model's performance: View the evaluation details for your model to determine how well it performs when introduced to new data.

  5. Deploy the model: Deploying a model makes it available for use via the Analyze API.

  6. Classify text: Use your custom model for sentiment analysis tasks.

Development options

Development option Description
Language studio Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up.
REST API Integrate sentiment analysis into your applications programmatically using the REST API.

For more information, see sentiment analysis quickstart.

Reference documentation

As you use Custom sentiment analysis, see the following reference documentation and samples for the Language service:

Development option / language Reference documentation Samples
REST APIs (Authoring) REST API documentation
REST APIs (Runtime) REST API documentation

Responsible AI

An AI system includes not only the technology, but also the people who use it, the people who will be affected by it, and the environment in which it's deployed. Read the transparency note for sentiment analysis to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:

Next steps