Enhancing Sentiment Analysis through Domain Adaptation and Ensemble Learning

Abstract

Sentiment analysis, the computational study of opinions and emotions expressed in text, is a critical component in many applications ranging from customer feedback analysis to social media monitoring. However, achieving robust sentiment analysis across diverse domains remains a significant challenge due to variations in language use, context, and domain-specific vocabulary. This paper presents a novel approach to enhancing sentiment analysis through the synergistic application of domain adaptation and ensemble learning techniques, incorporating both homogeneous and heterogeneous bootstrapping. We leverage state-of-the-art pretrained language models, fine-tuning them on specific domains to improve their sensitivity to domain-specific nuances. Our methodology includes comprehensive strategies for selecting and preprocessing data to facilitate effective domain adaptation. To further enhance model performance and robustness, we employ both homogeneous bootstrapping (using the same model architecture with different initializations) and heterogeneous bootstrapping (using diverse model architectures) as part of our ensemble learning framework. This dual approach allows us to capture a wide range of patterns and interactions within the data. The findings underscore the potential of combining domain adaptation with both homogeneous and heterogeneous bootstrapping techniques to address the inherent challenges of sentiment analysis in diverse and dynamic environments.