An Efficient Sarcasm Detection Using Linguistic Features and Ensemble Machine Learning

Sarcasm detection in written text has emerged as a significant research area within natural language processing (NLP). Sarcasm, characterized by conveying the opposite of the intended meaning often for humor, irony, or ridicule, poses a challenge due to its contextual and tonal nuances. This study investigates the application of machine learning methods to detect sarcasm in text due to its potential to reverse the overall sentiment expressed in a sentence. A total of 13 linguistic manually crafted features related to text meaning, word usage, lexical diversity, and readability are extracted. These features are then employed to train a variety of machine learning models including Gradient Boosting, Decision Tree, Random Forest, Support Vector Machine, Gaussian Nave Bayes, K-Nearest Neighbor, and Logistic Regression classifiers. Additionally, an Ensemble Model and a Dense Neural Network is developed, both trained on the extracted handcrafted features to showcase performance. The results reveal that the suggested ensemble model achieves a peak accuracy of 93% in sarcasm detection. The amalgamation of these 13 linguistic features enhances model performance when compared to other contemporary models, exhibiting an improvement of up to 5% in terms of F1-score using the publicly available gold standard News Headline Dataset.