Evaluation of Machine Learning Techniques for Motivational Quotes Classification and Categorization

Abstract

The effective classification and categorization of motivational quotes are critical for various applications in content management and recommendation systems. This paper presents a comprehensive methodology for consolidating and categorizing motivational quotes using machine learning techniques. The dataset, sourced from Kaggle’s Quotes-500K, contains over half a million quotes categorized into various tags such as ”Love,” ”Life,” ”Motivation,” and more. We address category redundancies by consolidating similar tags and selecting appropriate embedding models to capture semantic relationships between quotes. The embedding models are evaluated using t-SNE for finding the most distinctive model for representing motivational quotes. The final categorization focuses on three key categories: ”Love,” ”Inspirational,” and ”Humor”. We then evaluate the performance of several AI models, including Neural Networks, K-Nearest Neighbors (KNN), Euclidean distancebased classifiers, and Cosine Similarity methods. The proposed improved neural network model outperformed other models with a test accuracy of approximately 98%. These results suggest that our approach offers a robust solution for scalable and accurate categorization of motivational content. Future work will explore deeper NLP techniques, fine-tuning strategies for not just increased number of categories but further enhancing the categorization accuracy along with ranking them within their category.