Which functionality of Amazon Comprehend should the team use to meet these requirements?

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A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of “Sample Yogurt” in the document of customer comments, then “Sample Yogurt” should be classified as “yogurt,” “snack,” and “dairy product.”The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.

Which functionality of Amazon Comprehend should the team use to meet these requirements?

Custom classification with multi-class mode

Custom classification with multi-label mode

Custom entity recognition

Built-in models

Explanations:

Multi-class mode implies that each document can belong to only one category. Since products can be classified into multiple categories, this mode is not suitable.

Multi-label mode allows each document to belong to multiple categories, which fits the requirement that products can be classified into multiple related categories.

Custom entity recognition is for identifying specific entities (e.g., “Sample Yogurt”) in the text but does not directly handle the classification of products into multiple categories.

Built-in models are pre-trained models provided by Amazon Comprehend and may not support the custom classification requirements needed for multiple categories.

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