Transfer Learning in Aspect-Based Sentiment Analysis: Adapting Mental Health Insights to Enhance Product Reviews Evaluation

Abstract

"This study investigates enhancing sentiment analysis in product reviews by integrating insights from mental health discussions. While Aspect-Based Sentiment Analysis (ABSA) aims to associate sentiments with specific product features, it encounters limitations in adapting to diverse domains. Leveraging transfer learning, this research bridges the gap between mental health insights and product sentiment analysis. Analysing mental health-related text offers rich insights into emotional expressions and linguistic nuances, potentially enriching sentiment analysis in product reviews. The research aims to design an adaptable sentiment analysis model and contribute to transfer learning in Natural Language Processing (NLP) by exploring innovative approaches. Addressing gaps in existing literature, including limited exploration of mental health aspects, transferability of insights, evaluation metrics, model generalization, and fine-tuning strategies, the study seeks to refine sentiment analysis methodologies and establish a framework for broader transfer learning applications. The XLNet model displayed strong performance in sentiment analysis but yielded mixed results in aspect analysis. In terms of sentiment analysis, the model demonstrated impressive metrics, achieving an accuracy, F1 score, precision, and recall of 96.8%. The confusion matrix revealed effective classification of sentiments with minimal misclassifications. In contrast, the aspect analysis resulted in an accuracy of 76%, an F1 score of 75.3%, precision of 77.2%, and recall of 76%. The confusion matrix underscored challenges in accurately classifying the different aspects, indicating the need for further refinement to improve performance across all aspect categories. While the sentiment analysis proved robust, additional optimization and data enhancement are necessary for better aspect classification."