Machine Learning-Based Sentiment Analysis of Malaysia Food Tourism

This study the usage of sentiment analysis to enhance food tourism in Malaysia by utilizing natural language processing and machine learning. To address data imbalance and variability, a dataset of 360,000 food reviews was pre-processed using methods like text cleaning, tokenization, and resampling. Textual data was converted into machine learning inputs using feature extraction techniques like CountVectorizer and TF-IDF, and sentiments were categorized as either positive or negative. Cross-validation and hyperparameter tuning were used to assess four supervised algorithms including Support Vector Machines (SVM), Logistic Regression, Random Forest, and Multinomial Naive Bayes. With an accuracy of 84.15% on the test set, SVM with the TF-IDF vectorizer was the best-performing model, according to the results, closely followed by Logistic Regression. By combining text reviews with numerical ratings, a comprehensive understanding of tourist sentiments was obtained, providing useful information for enhancing tailored suggestions and promoting regional cuisines. This concept shows how sentiment analysis may boost tourism management and increase customer satisfaction. In order to improve sentiment analysis and its uses in tourism, future research could investigate sophisticated deep learning models and multimodal data sources.