Efficient Foreign Language Video Clip Labeling with SageMaker
Question
Your machine learning team is responsible for processing video clips posted to your company's Twitter social media account to understand the sentiment of the video clips.
Your team takes these video clips and labels them with the appropriate sentiment so that your marketing department can use them in their advertising campaigns.
You are now expanding into Spanish and Portuguese speaking regions of the world.
So you now need to translate video clip audio as a part of your sentiment labeling process. What AWS services and SageMaker built-in algorithms allow your team to label the foreign language video clips in the most efficient manner?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D. E.Answer: A.
Option A is CORRECT.
This is the most efficient option given.
It is accomplished completely using AWS managed services.
Comprehend can identify sentiment in Spanish and Portuguese language transcriptions.
You don't have to translate to English before using Comprehend.
Option B is incorrect.
Using the SageMaker seq2seq built-in algorithm overcomplicates the solution.
With this option, your team would have to build a machine learning model to translate the Spanish or Portuguese language to English when using the Translate service for this step.
Option C is incorrect.
Using the SageMaker Neural Topic Model built-in algorithm overcomplicates the solution.
With this option, your team would have to build a machine learning model to identify the sentiment in the video clips when you could just use the Comprehend service for this step.
Option D is incorrect.
Using the SageMaker BalzingText built-in algorithm overcomplicates the solution.
With this option, your team would have to build a machine learning model to identify the sentiment in the video clips when you could just use the Comprehend service for this step.
Option E is incorrect.
The Amazon Translate service requires the input of a text document.
In this option, you are attempting to translate directly from the audio of your video clip.
Reference:
Please see the Amazon Comprehend developer guide titled Languages Supported in Amazon Comprehend.
Please refer to the Amazon Translate developer guide titled What Is Amazon Translate?.
Please review the Amazon Translate developer guide titled How Amazon Translate Works.
The most efficient way to label foreign language video clips in this scenario would be to use AWS Transcribe, Comprehend, and Translate. The correct answer is A. Transcribe -> Comprehend -> Translate.
Here's how each of these services can be used in the process:
Transcribe: AWS Transcribe is a speech-to-text service that can convert audio and video recordings to text in multiple languages, including Spanish and Portuguese. It can handle noisy and low-quality audio, and it can identify multiple speakers in a recording. In this case, Transcribe can be used to transcribe the audio of the video clips into text.
Comprehend: AWS Comprehend is a natural language processing (NLP) service that can be used to extract insights from text. Comprehend can detect the sentiment of text in multiple languages, including Spanish and Portuguese. In this case, Comprehend can be used to analyze the text transcribed by Transcribe and determine the sentiment of the video clip.
Translate: AWS Translate is a neural machine translation service that can translate text from one language to another. It can translate text in multiple languages, including Spanish and Portuguese. In this case, Translate can be used to translate the text of the video clips into English so that the sentiment can be labeled and used by the marketing department.
Overall, the workflow would be as follows:
- Use Transcribe to transcribe the audio of the video clips into text in Spanish or Portuguese.
- Use Comprehend to determine the sentiment of the text.
- Use Translate to translate the text into English so that the sentiment can be labeled.
Using these services in this way can greatly improve the efficiency and accuracy of sentiment labeling for foreign language video clips.
