10.46243/jst.2024.v9.i01.pp139-147 registered

Dual Sentiment Analysis has emerged as a crucial and active research field. It involves extracting sentiment from comments, feedback, or critiques, which serves as valuable indicators for various purposes. To address this, we propose a novel dual training algorithm that utilizes both original and reversed training reviews to develop a robust sentiment classifier. Additionally, we introduce a dual prediction algorithm that comprehensively assesses both aspects of a review for classification during testing. The proposed approach goes beyond traditional polarity (positive-negative) classification by extending the framework to a 3-class system, which includes neutral reviews. This enhancement allows for a more nuanced understanding of sentiment. By considering neutral reviews, we gain deeper insights into the sentiment landscape. Dual Sentiment Analysis plays a pivotal role in helping companies gauge the level of acceptance of their products and formulate strategies to improve product quality. Moreover, it empowers policymakers and politicians to gain valuable insights by analyzing public sentiments on policies, public services, and political issues.