Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. Amazon Comprehend processes any text file in UTF-8 format. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. Use Amazon Comprehend to create new products based on understanding the structure of documents. For example, using Amazon Comprehend you can search social networking feeds for mentions of products or scan an entire document repository for key phrases.
You work with one or more documents at a time to evaluate their content and gain insights about them. Some of the insights that Amazon Comprehend develops about a document include:
- Entities – Amazon Comprehend returns a list of entities, such as people, places, and locations, identified in a document. For more information, see Detect Entities.
- Key phrases – Amazon Comprehend extracts key phrases that appear in a document. For example, a document about a basketball game might return the names of the teams, the name of the venue, and the final score. For more information, see Locate Key Phrases.
- Language – Amazon Comprehend identifies the dominant language in a document. Amazon Comprehend can identify 100 languages. For more information, see Detect the Dominant Language.
- Sentiment – Amazon Comprehend determines the emotional sentiment of a document. Sentiment can be positive, neutral, negative, or mixed. For more information, see Determine the Sentiment.
- Syntax – Amazon Comprehend parses each word in your document and determines the part of speech for the word. For example, in the sentence “It is raining today in Seattle,” “it” is identified as a pronoun, “raining” is identified as a verb, and “Seattle” is identified as a proper noun. For more information, see Analyze Syntax.
Customize Comprehend for your specific requirements without the skillset required to build machine learning-based NLP solutions. Using automatic machine learning, or AutoML, Comprehend Custom builds customized NLP models on your behalf, using data you already have. Training and calling custom comprehend models are both asynchronous (batch) operations.
Custom Classification: Create custom document classifiers to organize your documents into your own categories. For each classification label, provide a set of documents that best represent that label and submit the training data as a CSV file. You can have multiple document classifiers, each trained on a different set of input documents. Once a classifier is trained it can be used on any number of unlabeled document sets. Customers can use the console for a code-free experience or install the latest AWS SDK. For more information, see Custom Classification.
Custom Entities: Create custom entity types that analyze text for your specific terms and noun-based phrases. Customers can train custom entities to extract terms like policy numbers, or phrases that imply a customer escalation. To train the feature, customers need to provide a list of the entities (terms and phrases) and a set of documents that contain them, stored in S3. Once the model is trained, customers can submit analysis jobs against their model to extract their custom entities. For more information, see Custom Entity Recognition.
Document Clustering (Topic Modeling)
You can also use Amazon Comprehend to examine a corpus of documents to organize them based on similar keywords within them. Document clustering (topic modeling) is useful to organize a large corpus of documents into topics or clusters that are similar based on the frequency of words within them.
Topic modeling is a asynchronous process, you submit a set of documents for processing and then later get the results when processing is complete. Amazon Comprehend does topic modeling on large document sets, for best results you should include at least 1,000 documents when you submit a topic modeling job. For more information, see Topic Modeling.
The following examples show how you might use the Amazon Comprehend operations in your applications.
Example 1: Find documents about a subject
Find the documents about a particular subject using Amazon Comprehend topic modeling. Scan a set of documents to determine the topics discussed, and to find the documents associated with each topic. You can specify the number of topics that Amazon Comprehend should return from the document set.
Example 2: Find out how customers feel about your products
If your company publishes a catalog, let Amazon Comprehend tell you what customers think of your products. Send each customer comment to the
DetectSentiment operation and it will tell you whether customers feel positive, negative, neutral, or mixed about a product.
Example 3: Discover what matters to your customers
Use Amazon Comprehend topic modeling to discover the topics that your customers are talking about on your forums and message boards, then use entity detection to determine the people, places, and things that they associate with the topic. Finally, use sentiment analysis to determine how your customers feel about a topic.
Some of the benefits of using Amazon Comprehend include:
- Integrate powerful natural language processing into your apps—Amazon Comprehend removes the complexity of building text analysis capabilities into your applications by making powerful and accurate natural language processing available with a simple API. You don’t need textual analysis expertise to take advantage of the insights that Amazon Comprehend produces.
- Deep learning based natural language processing—Amazon Comprehend uses deep learning technology to accurately analyze text. Our models are constantly trained with new data across multiple domains to improve accuracy.
- Scalable natural language processing—Amazon Comprehend enables you to analyze millions of documents so that you can discover the insights that they contain.
- Integrate with other AWS services—Amazon Comprehend is designed to work seamlessly with other AWS services like Amazon S3, AWS KMS, and AWS Lambda. Store your documents in Amazon S3, or analyze real-time data with Kinesis Data Firehose. Support for AWS Identity and Access Management (IAM) makes it easy to securely control access to Amazon Comprehend operations. Using IAM, you can create and manage AWS users and groups to grant the appropriate access to your developers and end users.
- Encryption of output results and volume data —Amazon S3 already enables you to encrypt your input documents, and Amazon Comprehend extends this even farther. By using your own KMS key, you can not only encrypt the output results of your job, but also the data on the storage volume attached to the compute instance that processes the analysis job. The result is significantly enhanced security.
- Low cost—With Amazon Comprehend, you only pay for the documents that you analyze. There are no minimum fees or upfront commitments.