Which linguistic analysis technology first enabled large-scale retail sentiment analysis of customer reviews, revolutionizing how companies interpreted customer feedback?

The intersection of language technology and retail has transformed how businesses communicate with consumers. Natural language processing and linguistic analysis have become central to retail personalization algorithms, helping brands create more effective marketing messages and personalized shopping experiences. This poll tests your knowledge about a groundbreaking language-focused retail innovation that changed how companies analyze customer feedback.

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Word2Vec, developed by Google in 2013, which allowed retailers to map semantic relationships between words in customer reviews
BERT (Bidirectional Encoder Representations from Transformers), which provided contextual analysis of review language in 2018
Stanford's CoreNLP toolkit from 2010, which first enabled automated part-of-speech tagging in retail feedback systems
Amazon's Comprehend service launched in 2017, which pioneered entity recognition in customer review analysis