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Amazon Comprehend
Developer Guide

Topic Modeling

You can use Amazon Comprehend to examine the content of a collection of documents to determine common themes. For example, you can give Amazon Comprehend a collection of news articles, and it will determine the subjects, such as sports, politics, or entertainment. The text in the documents doesn't need to be annotated.

Amazon Comprehend uses an LDA-based learning model to determine the topics in a set of documents. It examines each document to determine the context and meaning of a word. The set of words that frequently belong to the same context across the entire document set make up a topic.

A word is associated to a topic in a document based on how prevalent that topic is in a document and how much affinity the topic has to the word. The same word can be associated with different topics in different documents based on the topic distribution in a particular document.

For example, the word "glucose" in an article that talks predominantly about sports can be assigned to the topic "sports," while the same word in an article about "medicine" will be assigned to the topic "medicine."

Each word associated with a topic is given a weight that indicates how much the word helps define the topic. The weight is an indication of how many times the word occurs in the topic compared to other words in the topic, across the entire document set.

For the most accurate results you should provide Amazon Comprehend with the largest possible corpus to work with. For best results, you should use at least 1,000 documents in each topic modeling job.

Topic modeling is an asynchronous process. You submit your list of documents to Amazon Comprehend from an Amazon S3 bucket using the StartTopicsDetectionJob operation. The response is sent to an Amazon S3 bucket. You can configure both the input and output buckets. Get a list of the topic modeling jobs that you have submitted using the ListTopicsDetectionJobs operation and view information about a job using the DescribeTopicsDetectionJob operation.

You can submit your documents two ways. The following table shows the options.

Format Description
One document per file Each file contains one input document. All files names must begin with a common prefix. This is best for collections of large documents.
One document per line The input is a single file. Each line in the file is considered a document. This is best for short documents, such as social media postings.

For more information, see the InputDataConfig data type.

After Amazon Comprehend processes your document collection, it returns a compressed archive containing two files, topic-terms.csv and doc-topics.csv. For more information about the output file, see OutputDataConfig.

The first output file, topic-terms.csv, is a list of topics in the collection. For each topic, the list includes, by default, the 10 terms most closely associated with a topic. For example, if you give Amazon Comprehend a collection of newspaper articles, it might return the following to describe the first two topics in the collection:

Topic Term Weight
000 team 26347.04884
000 game 18297.04884
000 player 15169.04884
000 season 14524.04884
000 play 12563.04884
000 yard 12298.04884
000 coach 10677.04884
000 games 10579.04884
000 football 10159.04884
000 quarterback 8628.048837
001 cup 12431.04884
001 food 9474.048837
001 minutes 8277.048837
001 add 7572.048837
001 tablespoon 6589.048837
001 oil 6202.048837
001 pepper 5362.048837
001 teaspoon 5341.048837
001 wine 5116.048837
001 sugar 5023.048837

You can specify the number of topics to return. For example, if you ask Amazon Comprehend to return 25 topics, it returns the 25 most prominent topics in the collection. Choose the number of topics based on your knowledge of the domain. It may take some experimentation to arrive at the correct number.

The second file, doc-topics.csv, lists the documents associated with a topic and the proportion of the document that is concerned with the topic. For example, Amazon Comprehend might return the following for a collection of documents:

Document Topic Proportion
sample-doc1 000 0.999330137
sample-doc2 000 0.998532187
sample-doc3 000 0.998384574
...    
sample-docN 000 3.57E-04

Amazon Comprehend utilizes information from the Lemmatization Lists Dataset by MBM, which is made available here under the Open Database License (ODbL) v1.0.

Role-based Data Access

To use the Amazon Comprehend topic modeling operations, you must grant Amazon Comprehend access to the Amazon S3 bucket that contains your document collection. For more information, see Role-Based Permissions Required for Topic Detection.

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