Sujin Butdisuwan

Papers

Total Citations

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About

Dr. Sujin Butdisuwan is a leading researcher in computational linguistics and artificial intelligence, with a primary focus on sentiment analysis and natural language processing. Her most influential work, "SPSO-EFVM: A Particle Swarm Optimization-Based Ensemble Fusion Voting Model for Sentence-Level Sentiment Analysis" (2024), has already garnered 4 citations, demonstrating its growing impact in the field. This groundbreaking contribution addresses critical challenges in human-robot integration, social platform monitoring, and decision-support systems by developing an innovative ensemble fusion voting model that significantly improves sentiment classification accuracy. Dr. Butdisuwan's research bridges the gap between traditional machine learning approaches and modern neural network architectures, offering practical solutions for real-world applications. Her work is particularly notable for introducing particle swarm optimization techniques to enhance ensemble learning methods, providing a robust framework for sentence-level sentiment analysis that outperforms conventional transformer-based models. As a researcher, she continues to push boundaries in AI-driven text analysis, making her contributions essential reading for students and scholars working at the intersection of computational linguistics and artificial intelligence.