Explanation:

Text Classification Model focused on Sentiment Analysis (B): This type of model is specifically designed to determine the overall sentiment of a text (positive. negative, or neutral). It is highly effective for classifying customer reviews at a high level, providing a clear understanding of the sentiment expressed in each review.
Text Classification Model with Aspect-Based Sentiment Analysis (ABSA) (C): BSA extends sentiment analysis by identifying sentiments associated with specific aspects c eatures mentioned in the text. For example, it can analyze mentions of product features (e.g. "battery life," "design") and determine whether the sentiment about those features is positive, negative, or neutral. This capability makes ABSA ideal for understanding the reasons behind the sentiment, aligning perfectly with the e-commerce company's requirements. Why not the others: • A. Named Entity Recognition (NER) Model: While NER is effective for identifying specific entities (e.g., product names, brands), it does noi perform sentiment analysis or extract the reasons for sentiment in text

You are working for an e-commerce company that wants to analyze customer reviews and determine the overall sentiment (positive, negative, or neutral) of each review. The company also wants to understand the reasons behind the sentiment, such as mentions of specific product features. Which type of generative AI model would be most effective in accomplishing this task? (Select two)

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Last updated: April 5, 2026 at 17:19

-3

Named Entity Recognition (NER) Model

5.4%

Text Classification Model focused on Sentiment Analysis

41.1%

Text Classification Model with Aspect-Based Sentiment Analysis (ABSA)

43.8%

Sequence-to-Sequence (Seq2Seq) Model for Text Generation

4.3%

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