Detect Offensive or Unsafe Content
Question
You work as a machine learning specialist for an online retail company that sells health products.
Your company allows users to enter reviews of the products they buy from the website.
You want to make sure the reviews do not contain any offensive or unsafe content, such as obscenities or threatening language. Which Amazon SageMaker algorithm or Amazon service will allow you to scan your user's review text in the simplest way?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: D.
Option A is incorrect.
The BlazingText algorithm is used for natural language processing tasks like sentiment analysis, and named entity recognition.
You should use all of these features when scanning your user's review text.
However, the BlazingText algorithm requires more developer effort and time than using the Comprehend service.
Option B is incorrect.
The Neural Topic Model algorithm is used to group documents into topics using the statistical distribution of words within the documents.
This algorithm would not be the most efficient choice for detecting offensive or unsafe language.
Option C is incorrect.
The Semantic Segmentation algorithm is used for computer vision applications.
So it is not an algorithm you would use for text analysis.
Option D is correct.
The Comprehend service scans your unstructured review text and analyzes it using SageMaker Natural Language Processing (NLP) algorithms to find key phrases, entities, and sentiments.
This is the most expeditious and efficient option.
Reference:
Please see the Amazon SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms, and the Amazon Machine Learning blog titled Analyze content with Amazon Comprehend and Amazon SageMaker notebooks.
Here is a diagram of the solution:
The Amazon service that can be used to scan user's review text for offensive or unsafe content is Amazon Comprehend. Comprehend is a natural language processing (NLP) service that uses machine learning to analyze text and provide insights such as sentiment analysis, entity recognition, and topic modeling.
Comprehend has the ability to identify offensive language, profanity, and threats in text. It also supports multiple languages, making it a useful tool for companies with a global customer base.
To use Comprehend, you can simply provide the review text as input, and the service will return a score indicating the level of offensive or unsafe content detected. This can be integrated into your existing review submission process to automatically flag and remove reviews that contain inappropriate content.
The other options provided in the question, BlazingText, Neural Topic Model (NTM), and Semantic Segmentation, are all machine learning algorithms available in Amazon SageMaker, but they are not designed specifically for detecting offensive or unsafe content in text. BlazingText is used for text classification and regression tasks, while NTM is used for topic modeling, and Semantic Segmentation is used for image analysis.