Authors : Shivam Goel; Sarthak Jain; Akshit Pundir; Yash Tyagi

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/m6d725tx

Scribd : https://tinyurl.com/43696upm

DOI : https://doi.org/10.38124/ijisrt/25apr1942

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Abstract : This project explores the intricate challenge of sarcasm detection in textual data using advanced Natural Language Processing (NLP) techniques. The primary goal is to create a model capable of accurately identifying and classifying sarcastic remarks within various contexts. We address this by leveraging Bidirectional Long Short-Term Memory (BiLSTM) networks, known for their ability to understand context by processing data in both forward and backward directions. To enhance semantic understanding, GloVe embeddings are employed to capture word relationships and contextual nuances. Our methodology encompasses comprehensive data preprocessing steps - such as tokenization, stopword removal, and lemmatization - to ensure clean and coherent text input. The BiLSTM model is trained on a diverse dataset that includes both sarcastic and non-sarcastic text samples, facilitating the learning of distinctive patterns. We evaluate the model’s performance using metrics like accuracy, precision, recall, and F1-score, anticipating that our model will effectively discern subtle sarcastic cues and outperform baseline methods. This study’s results have significant implications for sentiment analysis, social media monitoring, and conversational AI systems. Future directions include extending the model to handle multilingual sarcasm detection, integrating real-time data processing, and addressing ethical considerations in practical applications.

Keywords : NLP, Sarcasm Detection, BiLSTM, GloVe embeddings.

References :

1. Kolhatkar Varada - "Bharatsi: A Multi-task Dataset for Understanding Sarcasm in Indic Languages." arXiv:2201.06833 (2022)
2. Cai Ruijiao - "BERT-SR: A Hierarchical Transformer Network for Sarcasm Recognition." arXiv:2204.11088 (2022)
3. Zhou Yicheng - "A Dual-Process Model for Sarcasm Detection Based on Representation Learning." arXiv:2203.00124 (2022)
4. Devkota Prakash - "Sarcasm Detection Using Gated Recurrent Neural Networks with Attention Mechanism." arXiv:2106.09212 (2021)
5. Zhang Ziqiang - "Sarcasm Detection with Reinforcement Learning for Sparse Data." arXiv:2111.12225 (2021)
6. Brownlee, Jason - "How to Develop a Bidirectional LSTM For Sequence Classification in Python." Machine Learning Mastery (2020)
7. Devlin, Jacob et al - "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)  (2019)
8. Pennington, Jeffrey, Richard Socher, Christopher D. Manning - "GloVe: Global Vectors for Word Representation." (2014)