Domain-Specific Sentiment Analysis with Ensemble Techniques: Tailoring a Scalable Framework for Contextual Opinion Classification

Abstract

Sentiment analysis is a critical tool for extracting subjective information and understanding opinions in textual data. While general-purpose models perform well on broad datasets, they often lack contextual accuracy in specialized domains due to unique terminology and nuanced expressions. This study proposes a domain-specific extension of the Hybrid Ensemble Sentiment Analysis (HESA) framework to improve sentiment classification performance in specialized areas, particularly healthcare. The proposed framework combines ensemble techniques, including bagging and boosting, with customized domain-relevant features to capture subtle sentiments and enhance classification relevance. Traditional single-model approaches may struggle with contextual misinterpretation and scalability, whereas hybrid ensemble methods like HESA leverage multiple algorithms to improve generalization, reduce overfitting, and provide robust predictions across diverse datasets. The customized HESA model integrates industry-specific terminology, contextual phrases, and sentiment-bearing words unique to healthcare, allowing more precise sentiment interpretation. For instance, terms such as “relief,” “pain,” “recovery,” and “complication” carry domain-specific emotional significance that general models may overlook. The methodology includes constructing a comprehensive healthcare dataset and developing a feature set reflecting vocabulary, emotional cues, and contextual elements prevalent in healthcare narratives. Evaluation results demonstrate that the domain-specific HESA model consistently outperforms general sentiment models in accuracy and relevance. Case studies, including patient feedback and healthcare review analysis, confirm its effectiveness and scalability. These findings underscore the importance of domain customization in sentiment analysis and provide a foundation for applying tailored frameworks in other fields such as finance, law, and education, where domain-specific adaptations can substantially enhance the accuracy, contextual sensitivity, and practical utility of sentiment insights.