Text analytics
A collection of features from AI Language that extract, classify, and understand text within documents.
Get insights from text using natural language processing (NLP)
Mine insights in unstructured text using NLP—no machine-learning expertise required—using text analytics, a collection of features from AI Language. Gain a deeper understanding of customer opinions with sentiment analysis. Identify key phrases and entities such as people, places, and organizations to understand common topics and trends. Classify medical terminology using domain-specific, pretrained models. Evaluate text in a wide range of languages.
Broad entity recognition
Broad entity recognition
Identify important concepts in text, including key phrases and named entities such as people, events, and organizations.
Powerful sentiment analysis
Powerful sentiment analysis
Examine what customers are saying about your brand and analyze sentiments around specific topics through opinion mining.
Document summarization
Document summarization
Extract sentences that collectively convey the essence of a document.
Process medical text
Process medical text
Extract and process real-time and batch analysis of insights stored in unstructured medical text.
Identify and categorize important concepts
Identify and categorize important concepts
Extract a broad range of prebuilt entities such as people, places, organizations, dates/times, numerals, and more than 100 types of personally identifiable information (PII), including protected health information (PHI), in documents using named entity recognition.
Identify the main points in unstructured text
Identify the main points in unstructured text
Quickly evaluate and identify the main points in unstructured text. Get a list of relevant phrases that best describe a passage using key phrase extraction. Or identify sentences that best convey the main idea of a document with extractive summarization (preview).
Better understand customer perception
Better understand customer perception
Analyze positive and negative sentiment in social media, customer reviews, and other sources to get a pulse on your brand. Use opinion mining to explore customers' perception of specific attributes of products or services in text.
Process unstructured medical data
Process unstructured medical data
Extract insights from unstructured clinical documents such as doctors' notes, electronic health records, and patient intake forms using text analytics for health. Recognize, classify, and determine relationships between medical concepts such as diagnosis, symptoms, and dosage and frequency of medication.
Create a conversational layer over your data
Create a conversational layer over your data
Get answers to questions from semi-structured and unstructured content such as URLs, FAQ, product manuals, blogs, support documents, and more.
Automate your workflow
Automate your workflow
Automatically classify unstructured text and documents with custom text classification by using your domain-specific labels to improve decision making.
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Trusted by companies of all sizes
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Health services firm improves patient care
Kepro improves healthcare process with fast and accurate insights from text analytics for health.
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LaLiga increases fan engagement
LaLiga is engaging millions of fans around the world with a personal digital assistant, using Text Analytics to process incoming queries and determine user intent in multiple languages.
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Improving customer experience
Progressive Insurance levels up its chatbot journey and boosts customer experience with Azure AI.
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Text Request understands sentiments at scale
Software provider responds to customer sentiment and creates positive marketing experiences.
Text analytics resources and documentation
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Frequently asked questions about AI Language
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Text Analytics detects a wide range of languages, variants, and dialects. See the language support documentation for more information.
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Yes. Sentiment analysis and key phrase extraction are available for a select number of languages, and you may request additional languages in the Text Analytics Forum.
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Key phrase extraction eliminates nonessential words and standalone adjectives. Adjective-noun combinations, such as "spectacular views" or "foggy weather," are returned together. Generally, output consists of nouns and objects of the sentence, and is listed in order of importance. Importance is measured by the number of times a particular concept is mentioned, or the relation of that element to other elements in the text.
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Improvements to models and algorithms are announced if the change is major, and added to the service if the update is minor. Over time, you might find that the same text input results in a different sentiment score or key phrase output. This is a normal and intentional consequence of using managed machine-learning resources in the cloud.
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Yes, you can use the analyze operation in preview to combine more than one Text Analytics feature in the same asynchronous call. The analyze operation is currently only available in the Standard pricing tier and follows the same pricing criteria.
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Yes, these services and features are related: