How to Combine Text Analytics and Search using AWS Comprehend and Elasticsearch 6.0

Document Text Analytics using Amazon(AWS) Comprehend - Elasticsearch 6.0

How to automatically extract metadata from documents? How to index them and perform fast searches? In this post, we are going to see how to automatically extract metadata from a document using Amazon AWS Comprehend and Elasticsearch 6.0 for fast search and analysis.

This architecture we present improves the search and automatic classification of documents (using the metadata) for your organization.

Using the automatically extracted metadata you can search for documents and find what you need.

We are going to use the following components:

 

Architecture

AWS Comprehend and Elasticsearch

Example of applications:

  • Voice of customer analytics: You can use Amazon Comprehend to analyze customer interactions in the form of documents, support emails, online comments, etc., and discover what factors drive the most positive and negative experiences. You can then use these insights to improve your products and services.
  • Semantic 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.

 

When we talk about metadata, I like the following definition:

Metadata summarizes basic information about data, which can make finding and working with particular instances of data easier. For example, author, date created and date modified and file size are examples of very basic document metadata. Having the ability to filter through that metadata makes it much easier for someone to locate a specific document.

We are going to focus on the following metadata:

  • Document content type (PDF, Plain Text, HTML, Docx)
  • Document dominant language
  • Document entities
  • Key phrases
  • Sentiment
  • Document length
  • Country of origin of the document (metadata taken from the user details – ip address)

 

Amazon S3 will be the main documents storage. Once a document has been uploaded to S3 (you can easily use the AWS SDK to upload a document to S3 from your application) a notification is sent to an SQS queue and then consumed by a consumer.

The consumer gets the uploaded document and detects the entities/key phrases/sentiment using AWS Comprehend. Then it indexes the document to Elasticsearch. We use the Elasticsearch pre-processor plugins, Attachment Processor and Geoip Processor, to perform the other metadata extraction (more details below).

Here are the main steps performed in the process:

  1. Upload a document to S3 bucket
  2. Event notification from S3 to a SQS queue
  3. Event consumed by a consumer
  4. Entities/key phrases/sentiment detection using AWS Comprehend
  5. Index to Elasticsearch
  6. ES Ingestion pre-processing: extract document metadata using Attachment and Geoip Processor plugin
  7. Search in Elasticsearch by entities/sentiment/key phrases/language/content type/source country and full-text search
  8. Use Kibana for dashboard and search
  9. Use Skedler and Alerts for reporting, monitoring and alerting

In the example, we used AWS S3 as document storage. But you could extend the architecture and use the following:

  • SharePoint: create an event receiver and once a document has been uploaded extract the metadata and index it to Elasticsearch. Then search and get the document on SharePoint
  • Box, Dropbox and Google Drive: extract the metadata from the document stored in a folder and then easily search for them
  • Similar Object storage (i.e. Azure Blob Storage)

 

Event notification

When a document has been uploaded to the S3 bucket a message will be sent to an Amazon SQS queue. You can read more information on how to configure the S3 Bucket and read the queue programmatically here: Configuring Amazon S3 Event Notifications.

This is how a message notified from S3 looks. The information we need are the sourceIPAddress and object key

Consume messages from Amazon SQS queue

Now that the S3 bucket has been configured, when a document is uploaded to the bucket a notification will be sent to the SQS queue. We are going to build a consumer that will read this message and perform the instances/key phrases/sentiment detection using AWS Comprehend. You can eventually read a set of messages (change the MaxNumberOfMessages parameter) from the queue and run the task against a set of documents (batch processing).

With this code you can read the messages from a SQS queue and fetch the bucket and key (used in S3) of the uploaded document and use them to invoke AWS Comprehend for the metadata detection task:

We will download the uploaded document from S3.

AWS Comprehend

Amazon Comprehend is a new AWS service presented at the re:invent 2017.
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is, and automatically organizes a collection of text files by topic. – AWS Service Page

AWS Comprehend and Elasticsearch

It analyzes text and tells you what it finds, starting with the language, from Afrikaans to Yoruba, with 98 more in between. It can identify different types of entities (people, places, brands, products, and so forth), key phrases, sentiment (positive, negative, mixed, or neutral), and extract key phrases, all from a text in English or Spanish. Finally, Comprehend‘s topic modeling service extracts topics from large sets of documents for analysis or topic-based grouping. – Jeff Barr – Amazon Comprehend – Continuously Trained Natural Language Processing.

Instead of AWS Comprehend you can use similar service to perform Natural Language Processing, like: Google Cloud Platform – Natural Language API or Microsoft Azure – Text Analytics API.

 

 

Entities Detection

With this code, we can invoke the entities detection of AWS Comprehend. We will use the object key to download the object from S3.

Once you have downloaded the document, invoke the detect_entities method of AWS Comprehend.

Key phrases

To extract the key phrases use the detect_key_phrases method of AWS Comprehend.

Sentiment

To extract the sentiment (positive, negative, neutral) use the detect_sentiment method of AWS Comprehend.

Index to Elasticsearch

Given a document, we now have a set of metadata that identify it. Next, we index these metadata to Elasticsearch and use a pipeline to extract the other metadata. To do so, I created a new index called library and a new type called document.

Since we are going to use Elasticsearch 6.0 and Kibana 6.0, I suggested you read the following resource:

The document type we are going to create will have the following properties:

  • title: the title of the document (s3 key)
  • data: the base64 encoding of the document (used from the Attachment plugin to extract metadata)
  • ip: field that will contain the ip address of the user that uploaded the document (so we can extract the location details)
  • entities: the list of entities detected by AWS Comprehend
  • key phrases: the list of key phrases detected by AWS Comprehend
  • sentiment: the sentiment of the document detected by AWS Comprehend
  • s3Location: link to the document in the S3 bucket

Create a new index:

Create a new mapping. As you may notice, in ES 6.0, the type string has been replaced by the type text and keyword. String type ES 6.0

To pre-process documents before indexing it, we define a pipeline that specifies a series of processors. Each processor transforms the document in some way. For example, you may have a pipeline that consists of one processor that removes a field from the document followed by another processor that renames a field. Our pipeline will extract the document metadata (from the encoded base64) and the location information from the ip address.

The attachment processors use the Ingest Attachment plugin and the geoip processor use the Ingest Geoip plugin.

Read more about ingestion and pipeline here: Ingest Node, Pipeline Definition.

If you want, you can write your custom pre-processor and invoke AWS Comprehend in the ingestion phase: Writing Your Own Ingest Processor for Elasticsearch.

We can now index a new document:

This is how an indexed document looks like. Notice the attachment and geoip section. We have the language, content type, length and user location details.

Visualize, Report, and Monitor

With Kibana you can create a set of visualizations/dashboards to search for documents by entities and to monitor index metrics (like number of document by language, most contributing countries, document by content type and so on).

Using Skedler, an easy to use report scheduling and distribution application for Elasticsearch-Kibana-Grafana, you can centrally schedule and distribute custom reports from Kibana Dashboards and Saved Searches as hourly/daily/weekly/monthly PDF, XLS or PNG reports to various stakeholders. If you want to read more about it: Skedler Overview.

Example of Kibana dashboard:

Number of documents by language and countries that upload more documents.
AWS Comprehend and Elasticsearch

Countries by the number of uploaded documents.

AWS Comprehend and Elasticsearch

If you want to get notified when something happens in your index, for example, a certain entity is detected or the number of documents by country or documents by language reaches a certain value, you can use Alerts. It simplifies how you create and manage alert rules for Elasticsearch and it provides a flexible approach to notification (it supports multiple notifications, from Email to Slack and Webhook).

Conclusion

In this post we have seen how to use Elasticsearch as the search engine for documents metadata. You can extend your system by adding this pipeline to automatically extract the document metadata and index them to Elasticsearch for fast search (semantic search).

By automatically extracting the metadata from your documents you can easily classify and search (Knowledge management and discovery) for them by content, entities, content type, dominant content language and source country (from where the document has been uploaded).

I ran this demo using the following environment configurations:

  • Elasticsearch and Kibana 6.0.0
  • Python 3.4 and AWS SDK Boto3 1.4.8
  • Ubuntu 14.04
  • Skedler Reports and Alerts

Learn more about Automated Customizable Reporting  and Alerting

Learn more about Automated Customizable

Reporting  and Alerting