- A managed Natural Language Processing (NLP) service that you can use to extract meaningful information from unstructured texts so you can analyze them in a human-like context.
- It is an off-the-shelf solution that does not require deep machine learning expertise to get started.
- Works with social media feeds, web pages, comments, product reviews, articles, or emails.
- Can analyze texts in real-time by using built-in and custom models.
Common Use Cases
- Sentiment analysis for social media posts
- Organize documents by topics
- Knowledge management and discovery
- Classifies support tickets for better issue handling
- Medical cohort analysis
Amazon Comprehend generates insights in six (6) categories:
- Detects and categorizes real-world objects like date, organization, person, quantity, brands, or even a title given to a song or movie.
- Custom Entity Recognition
- Allows you to identify new entities that are not supported by the preset entities.
- This is useful if you want to extract entities that are specific only to your business, such as product codes.
- Detects and classifies emotions into neutral, positive, negative, or mixed.
- Detects the language used in a text by using identifiers from RFC 5646.
- Useful for multilingual companies or applications.
- Key Phrases
- A key phrase refers to a noun or a noun phrase that describes a particular thing.
- Personally Identifiable Information (PII)
- Determines sensitive information that could be used to identify a person, such as full name, birth date, bank account number, phone number, or email.
- Determine the different parts of speech used in the document, such as noun, pronoun, verb, adjective, adverb, etc.
- Each insight is associated with a confidence score.
- A confidence score is between 0 and 100, indicating the probability that a given prediction is correct.
- A product review with a positive sentiment and a 0.99 confidence score highly suggest positive feedback from a customer.
- Topic Modeling
- Classifies a collection of documents according to its common subject.
- For example, you can use Topic Modeling to categorize news articles into politics, sports, business, entertainment, etc.
- Comprehend custom
- It helps non-experts in machine learning build and train their own NLP models suited to their specific needs.
- Amazon Comprehend uses a machine learning method called transfer learning to train custom models.
- Charges are based on units where a single unit is equal to 100 characters.
- 3 unit (300 characters) minimum charge per request.
- All insights except for Syntax analysis are charged for $0.0001 per 10M units. Syntax Analysis is charged for $0.00005 per 10M units.
- Topic Modeling has a flat rate of $1.00 per job.