Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation

Understand

Sentiment analysis is a costly yet necessary task for enterprises to study the opinions of their customers to improve their products and to determine optimal marketing strategies.

• Due to the existence of a wide range of domains across different products and services, cross-domain sentiment analysis methods have received significant attention.
• These methods mitigate the domain gap between different applications by training cross-domain generalizable classifiers which help to relax the need for data annotation for each domain.
• Most existing methods focus on learning domain-agnostic representations that are invariant with respect to both the source and the target domains.