How do transformer models handle negation in sentiment analysis

I'm trying to understand how transformer models, such as BERT or GPT, handle negation in sentiment analysis. Specifically, I'm curious about how these models manage to correctly interpret sentences where negation changes the sentiment, such as "The movie is not good." A simple model using word embeddings + global averaging fails to handle negation properly. Intuitively, for example, if "good" has a positive sentiment score and "bad" has a negative sentiment score, a model might misinterpret "not good" by simply averaging the scores of "not" and "good". Example Without Negation Consider the following sentences with sentiment words: "The movie is good." "The movie is awesome." "The movie is terrible." Suppose we have the following word embeddings representing sentiment scores: "good" = [10] "awesome" = [12] "terrible" = [-10] Neutral words (assuming embeddings around 0): "the" = [0] "movie" = [0] "is" = [0] For these sentences, a simple global average of the sentiment scores works well: "The movie is good" = average([0, 0, 0, 10]) = 10 / 4 = 2.5 (positive sentiment) "The movie is awesome" = average([0, 0, 0, 12]) = 12 / 4 = 3 (positive sentiment) "The movie is terrible" = average([0, 0, 0, -10]) = -10 / 4 = -2.5 (negative sentiment) Example With Negation Now, consider the sentence "The movie is not good." In this case, the sentiment should be negative due to the presence of "not." However, averaging the scores naively might not handle this correctly. For example: "The movie is…