Dynamic Lexicon-Based Sentiment Analysis Architecture Using Nonlinear Feature Optimization

Abstract

Dynamic Lexicon-Based Sentiment Analysis (DLSA) improves sentiment detection by using adaptive, nonlinear feature optimization techniques. The model targets gaps in traditional methods, which often miss complex and subtle sentiment patterns. DLSA integrates modules for text preprocessing, lexicon and phrase extraction, and feature optimization with genetic algorithms. The system refines feature sets by selecting, combining, and adjusting features based on real-time performance data. Key findings show DLSA outperforms traditional models like SLPCF in accuracy, precision, and recall, especially in analyzing nuanced text data. This approach provides a flexible and efficient solution, enhancing the accuracy of sentiment classification in varied applications.