SARCASM DETECTION IN TWEETS USING SUPERVISED MACHINE LEARNING ALGORITHMS

Sarcasm is a complex linguistic phenomenon that poses a significant challenge for sentiment analysis in natural language processing (NLP). This paper presents a novel approach for sarcasm detection in tweets using supervised machine learning algorithms, specifically the Decision Tree, Multilayer Perceptron (MLP) Classifier, and AdaBoost Classifier. Our approach focuses on the extraction of salient features from tweets using advanced NLP techniques, which are then used to train the machine learning models. Firstly, we utilized NLP for feature extraction, which included sentiment polarity, emoticon analysis, punctuation usage, and n-grams. These features significantly enhance the contextual understanding required for sarcasm detection, as sarcasm often relies on subtleties of language and tone that are not easily captured by simple lexical analysis. Subsequently, we applied the Decision Tree algorithm to classify the tweets based on these features. We then utilized the MLP Classifier, which is known for its capability to model complex relationships, especially those found in text data. Finally, we employed the AdaBoost Classifier to combine these weak learners and improve the accuracy of sarcasm detection. Our study shows that incorporating these machine learning algorithms with NLP feature extraction techniques can significantly improve the accuracy of sarcasm detection in tweets. We believe our approach presents a valuable contribution to the field of sentiment analysis and can be adapted to other forms of social media text data to detect sarcasm and other nuanced sentiments