An automatic humor identification model with novel features from Berger's typology and ensemble models

Humor identification in text is essential for natural language understanding, impacting human-computer interactions and emotion recognition. This study introduces a novel approach to automatic humor identification in text using ensemble learning techniques. The objective is to classify text into two categories: humor and not humor. This research delves into humor detection in customer reviews, focusing on specific domains, and leverages diverse text features, including novel features derived from Berger's typology. State-of-the-art machine learning models, such as Support Vector Machine, Multilayer Perceptron, and Naïve Bayes, are employed. The study conducts an in-depth analysis of the Yelp review dataset, evaluating the performance of various ensemble learning methods. The proposed approach demonstrates exceptional results, achieving an 83.32% F-score with the Support Vector Machine classifier and an 85.68% accuracy using the stacking-based ensemble learning method with Random Forest on the Yelp Review dataset. Furthermore, this research includes an ablation study to assess the effectiveness of Berger's novel typology-based features in humor identification. This research contributes to the advancement of humor identification in text and highlights the effectiveness of ensemble learning techniques, especially when incorporating novel features inspired by Berger's typology.