Which linguistic concept has most significantly improved AI-based retail sentiment analysis in the past five years?

In retail sentiment analysis, sophisticated linguistic algorithms parse consumer language to determine attitudes toward products and brands. But which linguistic concept has revolutionized how AI understands consumer sentiment in recent years? Test your knowledge about the intersection of linguistics and retail analytics in this challenging poll about the technology that's transforming how brands understand customer feedback.

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Contextual embeddings that capture semantic nuance (like BERT and transformers that understand sentiment in context rather than just individual words)
Phonological pattern recognition (focusing on speech sounds and intonation patterns in customer service calls)
Morphological decomposition (breaking down words into meaningful parts to analyze sentiment at the morpheme level)
Prescriptive grammar analysis (evaluating sentiment based on adherence to formal language rules)