INTELLIGENT EMOTION RECOGNITION AND SENTIMENT CLASSIFICATION FROM TEXT USING ROBERTa

Abstract

In the digital era, the rapid growth of online reviews has significantly influenced consumer behavior and public opinion. These text-based sentiments play a crucial role in shaping decisions and perceptions. However, analyzing sentiments and emotions in written reviews presents unique challenges due to the complexity of human language, contextual variations, and the presence of sarcasm or ambiguity, particularly in low-resource languages like English. This study introduces a comprehensive framework for text-based sentiment analysis and emotion detection tailored to English reviews. The framework focuses solely on linguistic features such as lexical patterns, syntactic structures, and semantic relationships to capture the underlying emotions and opinions expressed in text. By leveraging advanced natural language processing and deep learning techniques, the system enhances the accuracy and depth of sentiment interpretation. A dedicated dataset of English text reviews has been developed to support this research, providing a valuable resource for future studies. The proposed approach is validated through a detailed case study, demonstrating its effectiveness and practical applicability in real-world scenarios.