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"Sarcasm Relation to Time: Sarcasm Detection with Temporal Features and" by Md Saifullah Razali, Alfian Abdul Halin et al.

This paper presents a deep learning-based framework to detect sarcasm in relation to time. Deep N-gram features generated using the FastText algorithm, combined with temporal handcrafted temporal features are used to train several machine learning classifiers. Experimental results show that Logistic Regression performs the best among all the classifiers. The introduction of the handcrafted temporal features has also whosn to improve overall detection performance when compared to existing works in the field. ....

Logistic Regression , Deep Learning , Natural Language Processing , Sarcasm Detection , Sentiment Analysis , Emporal Features ,

Can large language models detect sarcasm?

Can large language models detect sarcasm?
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Jacob Devlin , Juliann Zhou , York University , Sciencex Network , New York University , Natural Language Processing , Support Vector Machine , Long Short Term Memory , Large Language Models , State Of The Art Large Language Models , Sarcasm Detection ,

"Sarcasm Detection using Deep Learning with Contextual Features" by Md Saifullah Razali, Alfian Abdul Halin et al.

Abstract
Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with carefully handcrafted feature sets. These handcrafted feature sets are created based on their respective contextual explanations. Each feature sets are specifically designed for the sole task of sarcasm detection. The objective is to find the most optimal features. Some sets are good to go even when it is used in independence. Other sets are not really significant without any combination. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features are classified using a few machine learning techniques for comparison purposes. Logistic Regression is found to be the best classification algorithm for this task. Furthermore, result comparison to recent works and the ....

Convolutional Neural Network , Deep Learning , Deep Learning , Feature Extraction , Natural Language Processing , Sarcasm Detection , Ocial Networking Online , Task Analysis , ஆழமான கற்றல் , கேளுங்கள் பகுப்பாய்வு ,