Which NLP technique revolutionized retail recommendation systems by allowing algorithms to understand the contextual meaning of product reviews rather than just keywords?

Natural Language Processing (NLP) has transformed how retailers understand customer preferences and provide personalized experiences. Modern retail analytics systems leverage sophisticated linguistic algorithms to derive meaning from customer reviews, social media posts, and support interactions. This poll tests your knowledge about how language technology innovations are reshaping retail recommendation systems and creating more personalized shopping experiences through linguistic analysis.

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Word embeddings (like Word2Vec and GloVe) that represent words in vector space based on their semantic relationships
Basic sentiment analysis using positive/negative keyword dictionaries
Standard text frequency analysis (TF-IDF) that counts word occurrences
Simple rule-based parsing that identifies parts of speech in reviews