An Enhancement of Support Vector Machine in Context of Sentiment Analysis Applied in Scraped Data from Tripadvisor Hotel Reviews - Dela Cruz, Lambert T.; Racelis, Marjorie Jasmine C.; Agustin, Vivien C.; Guialil, Jamillah S.; Hill, Gabriel R.; Mahusay, Leisyl M.; Morano, Jonathan C. (International Journal of Computing Sciences Research, 2024)
Purpose – The purpose of this study is to improve the efficiency and accuracy of sentiment analysis in the context of hotel reviews, thereby contributing to the advancement of machine learning and natural language processing fields. Method – The study employs an Enhanced SVM algorithm, incorporating SMO, Random Search, and SMOTE, to address issues of long training time, hyperparameter optimization, and imbalanced data. Results – The Enhanced SVM outperforms the Traditional SVM, with a 13.48% increase in accuracy, a 100.42% reduction in training time, and improvements of 11.5% and 8.5% in Precision and F1-Score, respectively. Conclusion – The study successfully enhances the SVM algorithm, providing a more effective tool for sentiment analysis in the context of hotel reviews, with significant improvements in performance metrics. Recommendations – Future researchers should explore advanced optimization methods for hyperparameter tuning, use additional linguistic features like semantic analysis and context-aware embeddings, and incorporate sarcasm detection. Furthermore, consider deep learning models and ensemble approaches, combining SVM with other algorithms. Lastly, advocating for real-time sentiment analysis is suggested for immediate customer feedback insights. Research Implications – The study offers valuable insights into the application of machine learning techniques in sentiment analysis, particularly in the tourism industry. Practical Implications – The Enhanced SVM model can be used by platforms like TripAdvisor to provide more accurate sentiment analysis of hotel reviews, aiding tourists in their decision-making process. Social Implications – Improved sentiment analysis can enhance the overall travel experience, leading to more satisfying and informed travel decisions.