Amazon Comprehend is a natural language processing (NLP) service that uses machine
learning to find insights and
relationships in text. No machine learning experience required.
There is a treasure trove of potential sitting in your unstructured data. Customer
emails, support tickets, product
reviews, social media, even advertising copy represents insights into customer
sentiment that can be put to work for
your business. The question is how to get at it? As it turns out, Machine learning
is particularly good at accurately
identifying specific items of interest inside vast swathes of text (such as finding
company names in analyst reports),
and can learn the sentiment hidden inside language (identifying negative reviews, or
positive customer interactions with
customer service agents), at almost limitless scale.
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BENIFITS
Get better answers from your text Amazon Comprehend can discover
the meaning and relationships in text from customer support incidents, product reviews,
social media feeds, news articles, documents, and other sources. For example, you can
identify the feature that’s most
often mentioned when customers are happy or unhappy about your product.
Organize documents by topics Amazon Comprehend can analyze a
collection of documents and other text files (such as social media posts) and
automatically organize them by relevant terms or topics. You can then use the topics to
deliver personalized content to
your customers or to provide richer search and navigation. For example, if you have an
extensive collection of news
articles, you can automatically group them by subject matter to enable your site to
suggest new articles to visitors
based on what they’ve read previously.
Train models on your own data You can easily extend Amazon
Comprehend to identify specific terms, such as policy numbers or part codes. You can
also
extend Comprehend to classify documents and messages in a way that makes sense for your
business, like customer support
inquiries by request or social media posts by product. Adding this customization
requires no machine learning expertise.
You simply provide your labels and a small set of examples for each, and Comprehend
takes care of the rest.
Support for general and industry specific text Powered by
state-of-the-art machine learning models, Amazon Comprehend can discover insights from
unstructured text like
social media posts, emails, and web pages. Amazon Comprehend Medical also identifies
medical information, such as
medication and medical conditions, and determines their relationship to each other
(e.g., medicine dosage and strength).
For example, Amazon Comprehend Medical extracts “methicillin-resistant Staphylococcus
aureus,” often inputted as “MRSA,”
and provides context, such as whether a patient has tested positive or negative, to make
the extracted term meaningful.
Use cases
Voice of customer analytics
You can use Amazon Comprehend to analyze customer interactions in the
form of support emails, social media posts, online
comments, telephone transcriptions, etc., and discover what factors drive the most
positive and negative experiences.
You can then use these insights to improve your products and services.
More accurate search
You can use Amazon Comprehend to provide a better search experience by
enabling your search engine to index key phrases,
entities, and sentiment. This enables you to focus the search on the intent and the
context of the articles instead of
basic keywords.
Knowledge management and discovery
You can use Amazon Comprehend to organize and categorize your
documents by topic for easier discovery, and then
personalize content recommendations for readers by recommending other articles related
to the same topic.
Classify support tickets for better issue handling
Use custom classification to automatically categorize inbound customer
support documents, such as online feedback forms,
support tickets, forum posts, and product reviews based on their content. For example,
account cancellation requests,
billing problems, change of address, etc. Then, use custom entities to automatically
extract relevant information like
part numbers, loyalty tiers, and product names to quickly route documents the team best
equipped to solve the customer
problem and improve overall customer satisfaction.
