Domain Adaptation in Sentiment Classification

In this paper we analyse one of the most challenging problems in natural language processing: domain adaptation in sentiment classification. In particular, we look for generic features by making use of linguistic patterns as an alternative to the commonly feature vectors based on n-grams. The experimentation conducted shows how sentiment classification is highly sensitive to the domain from which the training data are extracted. However, the results of the experimentation also show how a model constructed around linguistic patterns is a plausible alternative for sentiment classification over some domains.