Data-driven vs. dictionary-based word n-gram feature induction for sentiment analysis

Abstract

We address the question which word n-gram feature induction approach yields the most accurate discriminative model for machine learning-based sentiment analysis within a specific domain: a purely data-driven word n-gram feature induction or a word n-gram feature induction based on a domain-specific or domain-non-specific polarity dictionary. We evaluate both approaches in document-level polarity classification experiments in 2 languages, English and German, for 4 analog domains each: user-written product reviews on books, DVDs, electronics and music. We conclude that while dictionary-based feature induction leads to large dimensionality reductions, purely data-driven feature induction yields more accurate discriminative models. © 2013 Springer-Verlag.