This paper describes a systematic approach for analyzing a dataset of 20,490 hotel reviews collected from TripAdvisor.com using SAS. The goal of this study is to answer several business questions related to customer satisfaction in the travel industry, including identifying the top 10 most frequently used words in hotel reviews, the most common words across all reviews, and the most commonly referenced entities in the reviews. The insights of the analysis can help managers improve their offerings to meet their target visitors’ needs and desires, thus leading to improved customer satisfaction and retention. Additionally, the study incorporates Logistic Regression (LR) as a powerful machine learning algorithm to predict sentiments in hotel reviews. The LR model shows favorable performance. The obtained results showcase favorable LR model performance, indicating the model’s proficiency in distinguishing between positive and negative sentiments and correctly classifying the samples.

This research examines the optimization of decision tree induction techniques by integrating evolutionary algorithms. It focuses on the Linear Genetic Programming Decision Tree (LGPDT). LGPDT employs a linear program to encode decision trees, achieving an optimal balance between accuracy and interpretability. The study introduces C-LGPDT as an extension of LGPDT, aiming to enhance its efficiency through correlation-based feature selection. This integration reduces dataset dimensionality and eliminates irrelevant or redundant features, resulting in a more accurate and interpretable decision tree model. The performance of C-LGPDT is thoroughly examined, and it is shown that it consistently outperforms older approaches, especially C4.5, and that it is more robust and accurate. A tourism dataset is also used to evaluate the C-LGPDT’s performance, with an emphasis on its stability in recall and precision. Results show that C-LGPDT is effective at solving decision tree induction problems, making it a good candidate for machine learning classification tasks.