Text-analysis

Sarcasm Detection using Deep Learning

Abstract

Sarcasm is the use of words intended to convey the opposite meaning, often to offend, irritate, or amuse. This project aims to recognize sarcasm in plain text using Natural Language Processing and Deep Learning techniques. The challenge lies in identifying sarcasm without contextual cues like tone, facial expressions, or body language. Our solution leverages machine learning models to distinguish between regular and sarcastic sentences.

Table of Contents

• Introduction
• Literature Survey
• Proposed System
• Implementation
• Results
• Conclusion
• References
• Acknowledgements

Introduction

Sentiment analysis combines Natural Language Processing (NLP) with machine learning to identify underlying sentiments in text. Sarcasm plays a crucial role in human social interaction and can significantly impact sentiment analysis results. Our project focuses on developing a reliable sarcasm detection system to improve sentiment analysis accuracy.

Key Features:

• Binary classification of sarcastic vs. non-sarcastic text
• Deep learning-based approach
• Web application interface
• Context-aware analysis

Literature Survey

We examined existing systems and their limitations:

Existing Approaches:

1. Contrast-based methods (positive/negative sentiment analysis)
2. Linguistic pattern recognition
3. Prosodic and structural variations
4. Contextual and environmental expertise

Limitations Identified:

• Contextual understanding challenges
• Dataset bias issues
• Ambiguity in sarcastic expressions
• Class imbalance problems
• Cultural variations in sarcasm

Proposed System

Our solution addresses these challenges through a comprehensive approach:

System Architecture

1. Data Collection: Compiled diverse dataset of sarcastic/non-sarcastic text
2. Preprocessing: Tokenization, stopword removal, punctuation handling
3. Feature Extraction: Word embeddings for semantic understanding
4. Model Selection: Evaluated RNNs, CNNs, and Transformer models
5. Training & Evaluation: Used accuracy, precision, recall metrics
6. Deployment: Web application for practical use

Technical Specifications

Software Requirements:

• OS: Windows
• Language: Python
• IDE: VS Code
• Libraries: Streamlit, Pandas, NLTK, re, string

Hardware Requirements:

• Processor: Intel i3 2.7GHz+
• RAM: 4GB+
• Storage: 256GB+

Implementation

Figure: Key libraries used in our implementation

Figure: Sarcasm Detection web interface

Results

Our model demonstrates promising performance in sarcasm detection:

• Achieved high accuracy in controlled environments
• Successfully identified contextual sarcasm cues
• Web application provides user-friendly interface

Conclusion

Sarcasm detection remains a challenging aspect of sentiment analysis. Our deep learning approach shows promising results, though challenges like cultural variations and dataset bias persist. Future work will focus on:

• Multimodal analysis (text + visual cues)
• Cross-language sarcasm detection
• Improved context understanding

References

1. Razali et al., “Sarcasm detection using deep learning with contextual features,” IEEE Access, 2021
2. Eke et al., “Context-based feature technique for sarcasm identification,” IEEE Access, 2021
3. Kumar & Garg, “Empirical study of shallow and deep learning models,” Journal of AIHC, 2023
4. Tan et al., “Sentiment analysis and sarcasm detection using deep multi-task learning,” Wireless PC, 2023

Acknowledgements

We sincerely thank:

• Dr. Mukesh D. Patil, Principal, RAIT
• Dr. Amarsinh V. Vidhate, HOD Computer Engineering
• Our supervisor Ms. Sakshi Somani
• Project coordinator Dr. Ekta Sarda
• Our team members: Atharva Badhe, Yashodeep Sonar, Sairaj Panwalkar, Varun Rane

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Project Team:

• Atharva Badhe (21CE1306)
• Yashodeep Sonar (21CE1409)
• Sairaj Panwalkar (21CE1419)
• Varun Rane (21CE1382)

Supervisor: Ms. Sakshi Somani
Institution: Ramrao Adik Institute of Technology, Navi Mumbai
Date: May 2024