Using Spectral Features to Improve Sentiment Analysis

A common approach to sentiment classification is to identify a set of sentiment-carrying words and then to use machine learning to build a classifier that can classify sentiment based on the presence/absence of those words. In this paper, we propose a Fourier-based extension of this approach. Specifically, we introduce a spectral learning algorithm that implicitly identifies sentiment-carrying words and higher-order functions of those words as it learns to assign real-valued sentiment scores to documents. The spectral learner extends the word presence model by applying Boolean logic operators (AND, OR, and XOR) to the word presence features to identify useful higher-order features. These spectral features can be used in other learning algorithms, and we show how the performance of other learning algorithms can be improved by these features. Finally, we consider the problem of determining which of a pair of reviews expresses more positive overall sentiment, and we show that the spectral learner can identify very small distinctions in sentiment with better-than-random accuracy, while larger distinctions can be correctly identified with high accuracy.