This is like giving your online store a smart assistant that can read all your product reviews, understand if customers are happy or unhappy, and summarize the mood for you automatically.
Manually reviewing thousands of product reviews to understand customer sentiment is slow, inconsistent, and expensive. This work uses large language models to automatically classify and interpret customer opinions in e-commerce reviews at scale.
Quality and scale of labeled review data, domain-specific prompt design or fine-tuning for e-commerce language, and integration into existing analytics and merchandising workflows.
Hybrid
Vector Search
Medium (Integration logic)
Inference latency and cost for running LLM-based sentiment classification at high review volumes, plus the need for efficient storage and retrieval of large amounts of review text.
Early Majority
Uses large language models rather than only traditional sentiment classifiers, enabling more nuanced understanding of context, sarcasm, and domain-specific expressions in e-commerce reviews.