Year: 2025
Type: article
Abstract: Sarcasm is a form of verbal irony where the intended meaning of a statement differs from its literal meaning. Detecting sarcasm is crucial for understanding sentiments and opinions, especially in social media analysis. However, sarcasm detection is challenging due to its reliance on context and tone. In this paper, we propose a sarcasm detection model using a Long Short-Term Memory (LSTM) network, a deep learning-based Recurrent Neural Network (RNN) variant. Our model is trained on social media datasets containing sarcastic and non-sarcastic statements. We compare its performance with the Word2Vec algorithm, analyzing accuracy and effectiveness. The proposed system aims to improve sarcasm classification in text-based data
Authors: Dr. Saroja T. V, Prathamesh Wagh, Prathamesh Wagh, Srushti Thombre
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409428600