A Novel Hybrid Lexicon and Economic Optimized kNN Framework for Sentiment Analysis in Tourism Platforms

Sentiment analysis in tourism platforms plays a vital role in understanding customer feedback, enhancing service quality, and supporting strategic economic decision-making across tourism markets. Challenges such as imbalanced sentiment classes, domain-specific language, and noisy data reduce the economic efficiency and analytical value of conventional approaches. This paper introduces a novel hybrid framework that combines lexicon-based sentiment and emotion analysis with an economically optimized weighted kNearest Neighbors (kNN) classifier. The framework incorporates advanced data augmentation techniques and comprehensive feature engineering, including n-gram TF-IDF extraction and metric learning—to improve minority sentiment class recognition and increase the economic robustness of predictive analytics. A modified co-optimization layer jointly tunes augmentation parameters, feature extraction methods, and classifier hyperparameters to maximize minority-class F1-scores while minimizing computational and economic costs. Experimental evaluations on real-world tourism review datasets demonstrate significant improvements in classification performance compared to baseline models such as SVM, Random Forest, and CNN, highlighting the framework’s economic value in large-scale tourism data processing. Additionally, a real-time business intelligence dashboard is developed for economic monitoring and dynamic visualization of sentiment trends and minorityclass heatmaps, enabling tourism stakeholders to make informed economic and managerial decisions and strategically respond to customer sentiments. The findings confirm a predominance of positive sentiments across tourism services while identifying economically critical areas requiring improvement. Future work will explore multilingual sentiment analysis and aspect-based models to enhance granularity, scalability, and economic impact. This research contributes an effective, interpretable, and economically oriented solution for advanced sentiment analysis in tourism platforms.