Lexical TF-IDF: An n-gram Feature Space for Cross-Domain Classification of Sentiment Reviews

Feature extraction and selection is a vital step in sentiment classification using machine learning approach. Existing methods use only TF-IDF rating to represent either unigram or n-gram feature vectors. Some approaches leverage upon the use of existing sentiment dictionaries and use the score of a unigram sentiment word as the feature vector and ignore TF-IDF rating. In this work, we construct n-gram sentiment features by extracting the sentiment words and their intensifiers or negations from a review. Then the score of an n-gram constructed from lexicon of semantic unigram and its intensifier or negation is multiplied to TF-IDF rating to determine the feature score. We experiment with two benchmark data sets for sentiment classification using Support Vector Machine and Maximum Entropy method with cross domain validation by considering training and testing data from two different sets and obtain a substantial improvement in terms of various performance measures compared to existing methods. Cross-domain validation ensures proposed method can be applied for sentiment classification of data sets where example patterns are not available, which typically is the case with commercial data sets.