Route-optimization models for hazardous-materials freight now routinely combine safety risk and environmental cost within a single multi-objective function, using increasingly sophisticated methods, genetic algorithms, entropy weighting, robust bilevel formulations, to balance the two. This article identifies a problem the weighting sophistication has not addressed: every reviewed model computes the emissions or fuel-consumption term as a fixed function of route distance, speed, and cargo load, treating the vehicle's combustion efficiency as a fixed constant that does not change with fuel-system operating condition. Engine-reliability data show fuel-system failure concentrated enough within heavy-duty diesel engines to make that constant-efficiency assumption a modeling choice, not an empirical fact. The article argues that improving how risk and emissions objectives are weighted against each other addresses a different problem from improving the fidelity of what generates the emissions number in the first place, and that current research effort concentrates almost entirely on the former. Closing that gap requires an emissions term that responds to a vehicle-condition input the reviewed literature currently has no place for, a distinction with direct consequences for how much confidence a Pareto-optimal hazmat route actually deserves.

Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.