Sarcasm Detection in Social Media Using Hybrid Deep Learning and Machine Learning Approaches

Sarcasm refers to the use of ironic language to convey the message. It is mainly used in social sites like Reddit, Twitter, etc. The identification of sarcasm improves sentiment analysis efficiency, which refers to analyzing people's behavior towards a particular topic or scenario. Our proposed methodology has used a hybrid supervised learning approach to detect the sarcastic patterns for classification. The supervised machine learning approaches include Logistic Regression, Naïve Bayes, Random Forest, and hybrid deep learning models like CNN and RNN. Before implementing the models, the dataset has been preprocessed. The data in the dataset is usually not fit for extracting features as it contains usernames, empty spaces, special characters, stop words, emoticons, abbreviations, hashtags, time stamps, URLs. Hence null values, stop words, punctuation marks, etc., are removed, and lemmatization is also done. After preprocessing, the proposed methodology has been implemented using various supervised machine learning models, hybrid neural network models, ensemble hybrid models, and models implementation by using word embeddings. The models have been implemented on two datasets. The outcome revealed that the hybrid neural network model RNN worked the best for both datasets and got the highest accuracy compared to other models.