Topic modeling - Amazon Comprehend

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 a Latent dirichlet allocation-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.

  • Each document should be at least 3 sentences long.

  • If a document consists of mostly numeric data, you should remove it from the corpus.

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. Content delivered to Amazon S3 buckets might contain customer content. For more information about removing sensitive data, see How Do I Empty an S3 Bucket? or How Do I Delete an S3 Bucket?.

Documents must be in UTF-8 formatted text files. 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. 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.

Each line must end with a line feed (LF, \n), a carriage return (CR, \r), or both (CRLF, \r\n). The Unicode line separator (u+2028) can't be used to end a line.

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 top terms by topic according to their weight. 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 0.118533
000 game 0.106072
000 player 0.031625
000 season 0.023633
000 play 0.021118
000 yard 0.024454
000 coach 0.016012
000 games 0.016191
000 football 0.015049
000 quarterback 0.014239
001 cup 0.205236
001 food 0.040686
001 minutes 0.036062
001 add 0.029697
001 tablespoon 0.028789
001 oil 0.021254
001 pepper 0.022205
001 teaspoon 0.020040
001 wine 0.016588
001 sugar 0.015101

The weights represent a probability distribution over the words in a given topic. Since Amazon Comprehend returns only the top 10 words for each topic the weights won't sum to 1.0. In the rare cases where there are less than 10 words in a topic, the weights will sum to 1.0.

The words are sorted by their discriminative power by looking at their occurrence across all topics. Typically this is the same as their weight, but in some cases, such as the words "play" and "yard" in the table, this results in an order that is not the same as the weight.

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. Amazon Comprehend can detect up to 100 topics in a 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. If you specified ONE_DOC_PER_FILE the document is identified by the file name. If you specified ONE_DOC_PER_LINE the document is identified by the file name and the 0-indexed line number within the file. For example, Amazon Comprehend might return the following for a collection of documents submitted with one document per file:

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.