Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media

Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve a...

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Bibliographic Details
Published in:Computers (Basel) 2023-11, Vol.12 (11), p.231
Main Authors: Olaniyan, Deborah, Ogundokun, Roseline Oluwaseun, Bernard, Olorunfemi Paul, Olaniyan, Julius, Maskeliūnas, Rytis, Akande, Hakeem Babalola
Format: Article
Language:English
Subjects:
Analysis
attention mechanism
Computational linguistics
COVID-19
Data mining
Datasets
Deep learning
Digital media
Irony
Language processing
Machine learning
Natural language interfaces
Natural language processing
Neural networks
sarcasm
Sentiment analysis
Social media
Social networks
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Summary:Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve an impressive accuracy of 99.86% in detecting and interpreting sarcasm and irony within social media text. Our approach involves innovating novel deep learning models adept at capturing subtle cues, contextual dependencies, and sentiment shifts inherent in sarcastic or ironic statements. Furthermore, we explore the potential of transfer learning from extensive language models and integrating multimodal information, such as emojis and images, to heighten the precision of sarcasm and irony detection. Rigorous evaluation against benchmark datasets and real-world social media content showcases the efficacy of our proposed models. The outcomes of this research hold paramount significance, offering a substantial advancement in comprehending intricate language nuances in digital communication. These findings carry profound implications for sentiment analysis, opinion mining, and an enhanced understanding of social media dynamics.
ISSN:2073-431X
2073-431X
DOI:10.3390/computers12110231