Leveraging Personalized Sentiment Lexicons for Sentiment Analysis

Abstract

We propose a novel personalized approach for the sentiment analysis task. The approach is based on the intuition that the same sentiment words can carry different sentiment weights for different users. For each user, we learn a language model over a sentiment lexicon to capture her writing style. We further correlate this user-specific language model with the user's historical ratings of reviews. Additionally, we discuss how two standard CNN and CNN+LSTM models can be improved by adding these user-based features. Our evaluation on the Yelp dataset shows that the proposed new personalized sentiment analysis features are effective.