A Capsule Neural Network (CNN) based Hybrid Approach for Identifying Sarcasm in Reddit Dataset

Introduction

With the surge in social media usage, sarcasm has become a popular mode of communication. However, detecting sarcasm in text remains a challenging task for natural language processing (NLP) due to its context-dependent nature. This post explores a novel approach to sarcasm detection using a hybrid deep learning model combining Capsule Neural Networks (CNN) with Long Short-Term Memory (LSTM) models. This method, discussed in a recent study, achieved a high accuracy of 95.6% on Reddit’s sarcastic comments dataset.

The Challenge of Sarcasm Detection

Sarcasm involves saying something contrary to one’s true intent, often relying on subtle cues. Detecting sarcasm in written form is challenging, as it lacks vocal tone and facial expressions. For instance, a comment like “Oh, great weather!” on a rainy day might seem positive but actually conveys dissatisfaction. Automated sarcasm detection has applications in fields like sentiment analysis, customer service, and social media monitoring, helping organizations accurately gauge public sentiment and respond accordingly.

Hybrid Deep Learning Approach

The study proposes a hybrid model using Capsule Neural Networks (CNN) and LSTM to capture intricate patterns and context in sarcastic comments. This approach stands out from traditional sarcasm detection methods that rely heavily on predefined linguistic features, as it incorporates powerful machine learning techniques for feature extraction and selection, including Word2Vec, TF-IDF, Principal Component Analysis (PCA), and Latent Dirichlet Allocation (LDA).

Methodology

The research uses the Self-Annotated Reddit Corpus (SARC), a dataset with over 1.3 million sarcastic comments tagged by users. After pre-processing steps like tokenization, stop-word removal, stemming, and lemmatization, features were extracted using Word2Vec and TF-IDF, followed by dimensionality reduction with PCA and LDA. The final model employs various deep learning architectures, including standalone CNN, LSTM, and combinations of LSTM+CNN and Capsule+CNN, with the Capsule+CNN achieving the highest accuracy at 95.6%.

For more detailed information, read the full text or refer to the study’s DOI link​(igmin137).

Results and Comparative Analysis

Through rigorous testing, the Capsule+CNN model demonstrated its ability to capture both local and global text patterns, outperforming other models in terms of accuracy. This method shows potential for use in real-time sarcasm detection across multiple platforms, adapting to various nuances in language.

• Capsule + CNN: 95.6%
• CNN: 95.5%
• LSTM + CNN: 89.4%
• LSTM: 87.6%

Conclusion

The integration of Capsule Neural Networks with CNN offers a powerful solution for sarcasm detection, pushing the boundaries of NLP applications in sentiment analysis and social media monitoring. Future directions include deploying this model in real-time applications and expanding its capabilities across other social media platforms.

Tags:
Sarcasm Detection, Capsule Neural Network, CNN, LSTM, Reddit Dataset, Sentiment Analysis, Natural Language Processing, Machine Learning.