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Power your website with on-demand translated reviews using Amazon Translate

The success of an ecommerce platform heavily relies on the reputation that has been established through thousands of user reviews and social shares by customers. By reviewing and sharing information existing customers establish a trust relationship with something they can’t physically touch. For this content to be accessible to a global audience it’s critical to translate it into the local language to help customers make their buying decisions.

Imagine that a company that sells older cars, boats, and motorcycles has the following problem: they are expanding their ecommerce business to several countries and they want to allow their customers to effortlessly read all reviews of their offerings written by other shoppers.

To solve this problem, we’ll show you how the company can leverage Amazon Translate to get on-demand translated reviews in real time. We’ll also show you how easily they ca integrate the service in a modern ecommerce architecture.

Amazon Translate is a high-quality neural machine translation service that uses advanced deep learning techniques to provide fast language translation of content from a source language to a target language, chosen among the supported pairs. It enables developers to easily invoke an API providing the text to be translated and obtain its translated version in real-time, hiding the complexity of building a neural machine translation model.

The ecommerce architecture

Our example website is a JavaScript single-page application that is hosted in a public Amazon S3 bucket where static website hosting has been enabled.

The example company wants to extend their global presence, so they have decided to use Amazon CloudFront to speed-up the distribution of their static web content worldwide. With CloudFront, files are delivered to end  users using a global network of edge locations that reduce latency and increase data transfer rates.

The website integrates with Login with Amazon and Amazon Cognito User Pools for user authentication and authorization, and makes REST API calls to an API deployed through Amazon API Gateway.

When API resources are requested, API Gateway invokes AWS Lambda functions that implement business logic operations like listing products, getting product details, adding user reviews, and translating user reviews.

More specifically ,the Lambda function code interacts with:

  • Amazon Translate, to get translated reviews
  • Amazon DynamoDB, to cache translated reviews in a fast and flexible NoSQL database and avoid invoking the Translate API for reviews that had already been translated
  • Amazon Comprehend, to analyse the sentiment of the reviews
  • Amazon Kinesis Data Firehose, to capture translation and sentiment analysis data into Amazon S3 for further analysis
  • Amazon RDS for Aurora, to store product and review data

Translating reviews and detecting language and sentiment

Wherever a user wants to translate a review, the website will make an API call to Amazon API Gateway that will execute a Lambda function that makes an API call to Amazon Translate. As you can see from the following code snippet you can see how easily you can make this call in Python by using the AWS SDK.

try:
       # Translate text
       result = translate_client.translate_text(Text=review, SourceLanguageCode=source_language, TargetLanguageCode=target_language)
       logger.info("Translation result: " +str(result))
except Exception as e:
       logger.error(str(e))
       raise e

The API call translate_text() needs only three parameters: text (in this case the review), source language (original language of the review), and target_language (the language into which you want to translate the review).

Obviously when a user writes a review we do not know which language the review is written in. The API call to post a new review will call the Lambda function PostReview. PostReview understands which language the review was written in by using Amazon Comprehendbefore saving it Amazon RDS for Aurora. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to understand the language. Amazon Comprehend is also very simple to invoke. Here the snippet of the code:

try:
       # Amazon Comprehend
       language = comprehend.detect_dominant_language(Text = review)
       logger.info("Language return: " + str(language))
except Exception as e:
       logger.error(str(e))
       raise e

When a review is posted, the Lambda function will also detect the sentiment that translate in an emoticon in the example website. To understand the sentiment of the review is also a single line of code:

try:
             # Amazon Comprehend
             sentiment = comprehend.detect_sentiment(Text=review, LanguageCode=language)
             logger.info("Sentiment ->: " + str(sentiment))
except Exception as e:
             logger.error(str(e))
             raise e

As you can see, both Amazon Comprehend detect_dominant_language() and detect_sentiment() API calls will just need the text as parameter.

Summary

Now that you’ve seen an example of how you can use Amazon Translate and Amazon Comprehend to empower your website, we hope you are inspired to create your own solutions.

Amazon Translate can get translations of a full sentence within a few milliseconds, enabling usage in both synchronous on-demand translations and in asynchronous tasks for executing a large number of translations with the aim of storing first and then delivering them.

Amazon Translate, Amazon Comprehend, Amazon Kinesis Data Firehose, Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Route 53, Amazon S3, Amazon CloudFront, Amazon API Gateway, Amazon Cognito are powerful services that allow you to implement fully-managed and serverless solutions for your business needs: the possible use cases are only limited by your imagination.

 


About the Authors

Giuseppe Angelo Porcelli is a Sr. Solutions Architect for Amazon Web Services in Italy. With several years engineering background, he helps enterprise customers designing flexible and resilient architectures using AWS services. His field of expertise covers Artificial Intelligence and Machine Learning. In free time, Giuseppe enjoys playing football.

 

 

 

 

Diego Natali is a solutions architect for Amazon Web Services in Italy. With several years engineering background, he helps ISV and Start up customers designing flexible and resilient architectures using AWS services. In his spare time he enjoys watching movies and riding his dirt bike.

 

 

 

 

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.