# Explanation for Why Logistic Regression can be so Accurate in Sentiment Classification?

My question is about how a logistic regression model performs so accurately. In some exploratory experimentation, I compared a logistic regression model against a long short term memory recurrent neural network for binary classification of consumer reviews (Positive or Negative)(25,000 reviews). The Logistic Regression model performed better with an accuracy of 88.0% and a recall, precision, and f-score of 0.88. The Recurrent Neural Net performed with an accuracy of 81.1%, a recall of 0.83, a precision of 0.80, and an f-score of 0.81. Perhaps my building of the network was flawed but I was still surprised to see that the Logistic Regression had such a high accuracy.