Which linguistic technology innovation first allowed retail chatbots to recognize and appropriately respond to customer sentiment (detecting frustration, satisfaction, etc.) in a commercially viable way?

The intersection of language technology and retail has transformed customer service in recent years. Natural Language Processing (NLP) advancements have enabled increasingly sophisticated retail chatbots that can understand context, sentiment, and even dialect variations. This poll tests your knowledge about a significant linguistic breakthrough in retail technology that changed how companies approach automated customer interactions.

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BERT (Bidirectional Encoder Representations from Transformers) in 2018
Word2Vec embeddings in 2013
Sequence-to-sequence models in 2014
Recurrent Neural Networks with LSTM in 2015