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|  How to Use Amazon Comprehend API for Text Analysis in Python

How to Use Amazon Comprehend API for Text Analysis in Python

October 31, 2024

Discover step-by-step instructions to leverage the Amazon Comprehend API for text analysis using Python. Enhance your data insights today!

How to Use Amazon Comprehend API for Text Analysis in Python

 

Integrating Amazon Comprehend API with Python

 

  • First, make sure that you have the AWS SDK for Python (Boto3) installed. If not, you can install it using pip:

 

pip install boto3

 

  • To start using Amazon Comprehend, you need to set up a Boto3 client in your Python script:

 

import boto3

# Create a Boto3 client for Amazon Comprehend
client = boto3.client('comprehend', region_name='us-west-2')

 

  • Now, you're ready to use various text analysis features of Amazon Comprehend. Let's see a few examples:

 

Language Detection

 

  • You can detect the dominant language of a piece of text as follows:

 

text = "Amazon Comprehend is a natural language processing service."

response = client.detect_dominant_language(Text=text)
language = response['Languages'][0]['LanguageCode']
confidence = response['Languages'][0]['Score']

print(f"Detected language: {language} with confidence {confidence}")

 

  • This code submits the text for language detection and prints out the detected language code along with the confidence score.

 

Sentiment Analysis

 

  • To perform sentiment analysis, you can submit text and get results indicating whether the sentiment is positive, negative, neutral, or mixed:

 

response = client.detect_sentiment(Text=text, LanguageCode='en')
sentiment = response['Sentiment']
sentiment_score = response['SentimentScore']

print(f"Sentiment: {sentiment} with scores {sentiment_score}")

 

  • Here, the API returns both an overall sentiment category and individual scores for each sentiment label.

 

Entity Recognition

 

  • To recognize entities such as persons, organizations, and locations in a text, use the following:

 

response = client.detect_entities(Text=text, LanguageCode='en')
entities = response['Entities']

for entity in entities:
    print(f"Entity: {entity['Text']} - Type: {entity['Type']} - Score: {entity['Score']}")

 

  • This allows you to pull specific entities from the text along with their types and confidence scores.

 

Key Phrase Extraction

 

  • To extract key phrases from the text that are central to its meaning, use the following:

 

response = client.detect_key_phrases(Text=text, LanguageCode='en')
key_phrases = response['KeyPhrases']

for phrase in key_phrases:
    print(f"Key Phrase: {phrase['Text']} - Score: {phrase['Score']}")

 

  • This returns a list of key phrases along with their confidence scores.

 

Notes and Best Practices

 

  • Always handle exceptions and errors when calling AWS services to catch issues related to IAM policies or service errors.
  •  

  • Consider the text size limitations for each API call as Comprehend limits the maximum size of the text you can analyze in a single request.
  •  

  • Make use of batching capabilities if you have multiple texts to analyze simultaneously, which can improve performance and cost efficiency.

 

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