How to use Amazon Comprehend operations using the AWS SDK for Python (Boto3)
2018.06.10
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We will demonstrate sample code to use the main functions and Topic Modeling that use Amazon Comprehend in the AWS SDK for Python (Boto3).
The main functions
- DetectDominantLanguage
- DetectEntities
- DetectKeyPhrases
- DetectSentiment
These functions also provide batch APIs that batch process up to 25 documents.
Functions used in Topic Modeling
- StartTopicsDetectionJob
- DescribeTopicsDetectionJob
- ListTopicsDetectionJobs
Sample Code
Let's start by looking at the main four functions.
import boto3
import json
# Comprehend constant
REGION = 'us-west-2'
# Function for detecting the dominant language
def detect_dominant_language(text):
comprehend = boto3.client('comprehend', region_name=REGION)
response = comprehend.detect_dominant_language(Text=text)
return response
# Function for detecting named entities
def detect_entities(text, language_code):
comprehend = boto3.client('comprehend', region_name=REGION)
response = comprehend.detect_entities(Text=text, LanguageCode=language_code)
return response
# Function for detecting key phrases
def detect_key_phraes(text, language_code):
comprehend = boto3.client('comprehend', region_name=REGION)
response = comprehend.detect_key_phrases(Text=text, LanguageCode=language_code)
return response
# Function for detecting sentiment
def detect_sentiment(text, language_code):
comprehend = boto3.client('comprehend', region_name=REGION)
response = comprehend.detect_sentiment(Text=text, LanguageCode=language_code)
return response
def main():
# text
text = "Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text."
# language code
language_code = 'en'
# detecting the dominant language
result = detect_dominant_language(text)
print("Starting detecting the dominant language")
print(json.dumps(result, sort_keys=True, indent=4))
print("End of detecting the dominant language\n")
# detecting named entities
result = detect_entities(text, language_code)
print("Starting detecting named entities")
print(json.dumps(result, sort_keys=True, indent=4))
print("End of detecting named entities\n")
# detecting key phrases
result = detect_key_phraes(text, language_code)
print("Starting detecting key phrases")
print(json.dumps(result, sort_keys=True, indent=4))
print("End of detecting key phrases\n")
# detecting sentiment
result = detect_sentiment(text, language_code)
print("Starting detecting sentiment")
print(json.dumps(result, sort_keys=True, indent=4))
print("End of detecting sentiment\n")
if __name__ == '__main__':
main()
DetectDominantLanguage operation.
It can detect the dominant language of the document. Amazon Comprehend can detect 101 different languages.
Execution result
{
"Languages": [
{
"LanguageCode": "en",
"Score": 0.9940536618232727
}
],
"ResponseMetadata": {
"HTTPHeaders": {
"connection": "keep-alive",
"content-length": "64",
"content-type": "application/x-amz-json-1.1",
"date": "Thu, 22 Mar 2018 04:15:20 GMT",
"x-amzn-requestid": "a29fda00-2d87-11e8-ad56-************"
},
"HTTPStatusCode": 200,
"RequestId": "a29fda00-2d87-11e8-ad56-************",
"RetryAttempts": 0
}
}
DetectEntities operation.
It can detect the entities, such as persons or places, in the document.
Execution result
{
"Entities": [
{
"BeginOffset": 0,
"EndOffset": 6,
"Score": 0.8670787215232849,
"Text": "Amazon",
"Type": "ORGANIZATION"
},
{
"BeginOffset": 7,
"EndOffset": 17,
"Score": 1.0,
"Text": "Comprehend",
"Type": "COMMERCIAL_ITEM"
}
],
"ResponseMetadata": {
"HTTPHeaders": {
"connection": "keep-alive",
"content-length": "201",
"content-type": "application/x-amz-json-1.1",
"date": "Thu, 22 Mar 2018 04:15:20 GMT",
"x-amzn-requestid": "a2b84450-2d87-11e8-b3f9-************"
},
"HTTPStatusCode": 200,
"RequestId": "a2b84450-2d87-11e8-b3f9-************",
"RetryAttempts": 0
}
}
DetectKeyPhrases operation.
It detects key phrases in the document contents.
Execution result
{
"KeyPhrases": [
{
"BeginOffset": 0,
"EndOffset": 17,
"Score": 0.9958747029304504,
"Text": "Amazon Comprehend"
},
{
"BeginOffset": 21,
"EndOffset": 50,
"Score": 0.9654422998428345,
"Text": "a natural language processing"
},
{
"BeginOffset": 52,
"EndOffset": 55,
"Score": 0.941932201385498,
"Text": "NLP"
},
{
"BeginOffset": 57,
"EndOffset": 64,
"Score": 0.9076098203659058,
"Text": "service"
},
{
"BeginOffset": 75,
"EndOffset": 91,
"Score": 0.872683584690094,
"Text": "machine learning"
},
{
"BeginOffset": 100,
"EndOffset": 126,
"Score": 0.9918361902236938,
"Text": "insights and relationships"
},
{
"BeginOffset": 130,
"EndOffset": 134,
"Score": 0.998969554901123,
"Text": "text"
}
],
"ResponseMetadata": {
"HTTPHeaders": {
"connection": "keep-alive",
"content-length": "615",
"content-type": "application/x-amz-json-1.1",
"date": "Thu, 22 Mar 2018 04:15:21 GMT",
"x-amzn-requestid": "a2d409a7-2d87-11e8-a9a6-************"
},
"HTTPStatusCode": 200,
"RequestId": "a2d409a7-2d87-11e8-a9a6-************",
"RetryAttempts": 0
}
}
DetectSentiment operation.
It detects the emotion (positive, negative, mixed, or neutral) in the document contents.
Execution result
{
"ResponseMetadata": {
"HTTPHeaders": {
"connection": "keep-alive",
"content-length": "161",
"content-type": "application/x-amz-json-1.1",
"date": "Thu, 22 Mar 2018 04:15:21 GMT",
"x-amzn-requestid": "a2ebb00b-2d87-11e8-9c58-************"
},
"HTTPStatusCode": 200,
"RequestId": "a2ebb00b-2d87-11e8-9c58-************",
"RetryAttempts": 0
},
"Sentiment": "NEUTRAL",
"SentimentScore": {
"Mixed": 0.003294283989816904,
"Negative": 0.01219215989112854,
"Neutral": 0.7587229609489441,
"Positive": 0.2257905900478363
}
}
Topic Modeling
Let’s try executing the topic detection job.
Sample Code
import boto3
import json
import time
from bson import json_util
# Comprehend constant
REGION = 'us-west-2'
# A low-level client representing Amazon Comprehend
comprehend = boto3.client('comprehend', region_name=REGION)
# Start topics detection job setting
input_s3_url = "s3://your_input"
input_doc_format = "ONE_DOC_PER_FILE or ONE_DOC_PER_FILE"
output_s3_url = "s3://your_output"
data_access_role_arn = "arn:aws:iam::aws_account_id:role/role_name"
number_of_topics = 10
job_name = "Job_name"
input_data_config = {"S3Uri": input_s3_url, "InputFormat": input_doc_format}
output_data_config = {"S3Uri": output_s3_url}
# Starts an asynchronous topic detection job.
response = comprehend.start_topics_detection_job(NumberOfTopics=number_of_topics,
InputDataConfig=input_data_config,
OutputDataConfig=output_data_config,
DataAccessRoleArn=data_access_role_arn,
JobName=job_name)
# Gets job_id
job_id = response["JobId"]
print('job_id: ' + job_id)
# It loops until JobStatus becomes 'COMPLETED' or 'FAILED'.
while True:
result = comprehend.describe_topics_detection_job(JobId=job_id)
job_status = result["TopicsDetectionJobProperties"]["JobStatus"]
if job_status in ['COMPLETED', 'FAILED']:
print("job_status: " + job_status)
break
else:
print("job_status: " + job_status)
time.sleep(60)
# You can get a list of the topic detection jobs that you have submitted.
input_data_config = {"S3Uri": input_s3_url, "InputFormat": input_doc_format}
filter_job_name = {"JobName": job_name}
topics_detection_job_list = comprehend.list_topics_detection_jobs(Filter=filter_job_name)
print('topics_detection_job_list: ' + json.dumps(topics_detection_job_list,
sort_keys=True,
indent=4,
default=json_util.default))
StartTopicsDetectionJob
Start topics detection job as an asynchronous operation.
You can confirm job status using DescribeTopicDetectionJob after get JobId.
There are two kinds of InputFormat. - ONE_DOC_PER_FILE - When one document is included in each file. - ONE_DOC_PER_LINE - When one file, each line of the file is considered a document.
DescribeTopicDetectionJob
Get job status for topic detection job. There are four statuses as follows:
- JobStatus
- SUBMITTED
- IN_PROGRESS
- COMPLETED
- FAILED
In this sample code, we escape the while loop when JobStatus is COMPLETED or FAILED.
ListTopicsDetectionJobs
Get list of topic detection job.
Execution result
job_id: 2733262c2747153ab8cb0b01********
job_status: SUBMITTED
job_status: IN_PROGRESS
[...]
job_status: COMPLETED
topics_detection_job_list: {
"ResponseMetadata": {
"HTTPHeaders": {
"connection": "keep-alive",
"content-length": "415",
"content-type": "application/x-amz-json-1.1",
"date": "Thu, 22 Mar 2018 04:27:59 GMT",
"x-amzn-requestid": "669ffb28-2d89-11e8-82a0-************"
},
"HTTPStatusCode": 200,
"RequestId": "669ffb28-2d89-11e8-82a0-************",
"RetryAttempts": 0
},
"TopicsDetectionJobPropertiesList": [
{
"EndTime": {
"$date": 1521692818930
},
"InputDataConfig": {
"InputFormat": "ONE_DOC_PER_FILE",
"S3Uri": "s3://your_input"
},
"JobId": "2733262c2747153ab8cb0b01********",
"JobName": "Job4",
"JobStatus": "COMPLETED",
"NumberOfTopics": 10,
"OutputDataConfig": {
"S3Uri": "s3://your_output/**********-2733262c2747153ab8cb0b01********-1521692274392/output/output.tar.gz"
},
"SubmitTime": {
"$date": 1521692274392
}
}
]
}
Check the output file
Check that a file is created in the S3 bucket at the output destination.
You can confirm using ListTopicsDetectionJobs.
- OutputDataConfig
"OutputDataConfig": {
"S3Uri": "s3://your_output_bucket/************-700e040bd7ae56714b65f56049f574d1-1521592942171/output/output.tar.gz"
},
$ aws s3 cp s3://your_output_bucket/************-700e040bd7ae56714b65f56049f574d1-1521592942171/output/output.tar.gz .
$ tar -zxvf output.tar.gz
x topic-terms.csv
x doc-topics.csv
- topic-terms.csv
- List os topics in the collection. Each topic, by default, contains top terms according to topic weighting.
- doc-topics.csv
- Lists the documents associated with the topic and the percentage of documents related to that topic. ß Note: To get best results you need to use at least 1,000 documents with each topic modeling job.
Conclusion
Since I think the API to use Amazon Comprehend will be useful, I have introduced sample code of each function using the AWS SDK for Python (boto3).
