AI summary

This paper extends the Structural Correspondence Learning (SCL) algorithm for sentiment classification, reducing relative error by 30-46%, and introduces an A-distance measure for domain similarity, aiding in selecting source domains for improved classifier transferability across product review datasets from Amazon (books, DVDs, electronics, kitchen appliances).

Abstract

Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains.