What is sentiment analysis and opinion mining in Azure Cognitive Service for Language?
Sentiment analysis and opinion mining are features offered by Azure Cognitive Service for Language, 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.
- Quickstarts are getting-started instructions to guide you through making requests to the service.
- How-to guides contain instructions for using the service in more specific or customized ways.
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 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.
To use this feature, you submit data for analysis and handle the API output in your application. Analysis is performed as-is, with no additional customization to the model used on your data.
Create an Azure Language resource, which grants you access to the features offered by Azure Cognitive Service for Language. It will generate a password (called a key) and an endpoint URL that you'll use to authenticate API requests.
Send the request containing your data as raw unstructured text. Your key and endpoint will be used for authentication.
Stream or store the response locally.
Get started with sentiment analysis
To use sentiment analysis, you submit raw unstructured text for analysis and handle the API output in your application. Analysis is performed as-is, with no additional customization to the model used on your data. There are two ways to use sentiment analysis:
|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. For more information, see the Language Studio website or language studio quickstart.|
|REST API or Client library (Azure SDK)||Integrate sentiment analysis into your applications using the REST API, or the client library available in a variety of languages. For more information, see the sentiment analysis quickstart.|
|Docker container||Use the available Docker container to deploy this feature on-premises. These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons.|
An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is 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:
Reference documentation and code samples
As you use this feature in your applications, see the following reference documentation and samples for Azure Cognitive Services for Language:
|Development option / language||Reference documentation||Samples|
|REST API||REST API documentation|
|C#||C# documentation||C# samples|
|Java||Java documentation||Java Samples|
|Python||Python documentation||Python samples|
There are two ways to get started using the entity linking feature: