Cross-domain Sentiment Classification using an Adapted Naïve Bayes Approach and Features Derived from Syntax Trees

Online product reviews contain information that can assist in the decision making process of new customers looking for various products. To assist customers, supervised learning algorithms can be used to categorize the reviews as either positive or negative, if large amounts of labeled data are available. However, some domains have few or no labeled instances (i.e., reviews), yet a large number of unlabeled instances. Therefore, domain adaptation algorithms that can leverage the knowledge from a source domain to label reviews from a target domain are needed. We address the problem of classifying product reviews using domain adaptation algorithms, in particular, an Adapted Naı̈ve Bayes classifier, and features derived from syntax trees. Our experiments on several cross-domain product review datasets show that this approach produces accurate domain adaptation classifiers for the sentiment classification task.