Trends and Classification Performance in LLM Survey Data Analysis

Abstract

This paper explores trends in survey data over time, evaluates the distribution of taxonomies, and compares different classification algorithms, including Naive Bayes, Random Forest, and Support Vector Machines (SVM). The performance of each classifier is compared using accuracy, precision, recall, and F1-score. It is found that Naive Bayes yields the best accuracy for this dataset, especially when handling imbalanced data using SMOTE.