The data is correct and a-biased. If you ask 100 people around you, they are, on average, more likely to have had a negative burrito experience, than a negative pasta experience.

The learning algorithms are crude and dumb. They will simply fit to any data you provide it (you choose how many Mexican restaurant food reviews you train your sentiment classifier on). Then they count how many times the words "mexican" and "man" and "mexican man" appear with a positive or negative label in the train set. And objectively try to give the best probability for that.

Current sentiment analyzers are not AI: no common sense, no understanding, no reasoning. We are just rushing to replace looking a job candidate in the eyes with running some 1960's logistic regression over their cover letter. Let's hope for their sake they did not manage a Mexican restaurant.