Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis

Sentiment analysis is a costly yet necessary task for enterprises to study the opinions of their costumers to improve their products and services and to determine optimal marketing strategies. Due to existence of a wide range of domains across different products and services, cross-domain sentiment analysis methods have received significant attention in recent years. These methods mitigate the domain gap between different applications by training cross-domain generalizable classifiers which help to relax the need for individual data annotation per each domain. Most existing methods focus on learning domain-agnostic representations that are invariant with respect to both the source and the target domains. As a result, a classifier that is trained using annotated data in a source domain, would generalize well in a related target domain. In this work, we introduce a new domain adaptation method which induces large margins between different classes in an embedding space based on the notion of prototypical distribution. This embedding space is trained to be domain-agnostic by matching the data distributions across the domains. Large margins in the source domain help to reduce the effect of ``domain shift'' on the performance of a trained classifier in the target domain. Theoreticaland empirical analysis are provided to demonstrate that the method is effective.