Enterprise AI Analysis
An emoji centric approach to sarcasm detection in online discourse
This paper introduces an emoji-focused approach for sarcasm detection in online discourse, leveraging machine learning and fine-tuned BERT models. It demonstrates the critical role of emojis, even without other contextual modalities, and presents novel sarcasm-aware GloVe-based emoji embeddings that outperform existing methods. The approach also investigates how text and emoji sentiment, frequency, and position influence sarcasm classification, achieving high F1, MCC, and RoC_AuC scores on unseen datasets. This work has significant implications for identifying cyberbullying and hateful content.
Executive Impact
Key metrics demonstrating the power of emoji-centric sarcasm detection in real-world applications.
29,377
Emoji-containing texts processed
1.14%
F1-score improvement in SarcOjiTest2 (ESR)
65.32%
Highest F1-score (BERT) on SarcOjiTest2
Deep Analysis & Enterprise Applications
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Sarcasm-aware Emoji Embedding Training
Emoji Input
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Emoji Embedding (Trainable)
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Extract Emoji Embeddings
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Text Input
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GloVe Word Embeddings
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LSTM
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Concatenate
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Tanh
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Dropout
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Sigmoid
| Model | Word2Vec + Emoji Description | GloVe + Proposed Sarcasm-aware |
|---|---|---|
| TextWithEmojisVader | F1: 31.51%, MCC: 0.10, AUC: 55.01% | F1: 53.07%, MCC: 0.15, AUC: 50.56% |
| TextMojiVaderESRPos | F1: 32.00%, MCC: 0.10, AUC: 55.76% | F1: 65.21%, MCC: 0.17, AUC: 56.74% |
| TextMojiVaderESRPosEfreq | F1: 33.12%, MCC: 0.11, AUC: 55.67% | F1: 64.97%, MCC: 0.18, AUC: 57.09% |
| Note: Significant values are in bold within the paper, indicating superior performance. | ||
17.4%
Average improvement in F1-scores on SarcOjiTest1 with MaxEmoji (ML-based classifiers)
67.36%
Sarcastic records in training data with MaxEmoji towards the end of text
21.75%
Occurrences of 'rolling eyes' emoji in sarcastic texts (often misclassified)
Emoji's Impact on Sarcasm Detection
The study demonstrates that the inclusion of emojis significantly improves sarcasm detection, particularly when using 'Emojional' and newly developed 'Sarcasm-aware GloVe embeddings'. Models performed poorer with description-based emoji embeddings. The 'MaxEmoji' (most frequent emoji) plays a critical role, outperforming models that consider all emojis. Its position, especially at the end of the text, yields the best results. Transformer-based classifiers achieved higher F1, MCC, and RoC_AuC scores, showing better generalization capabilities across datasets.
Key Outcomes:
- Emojis boost F1, MCC, and RoC_AuC scores for sarcasm detection.
- Sarcasm-aware GloVe embeddings outperform other emoji embeddings.
- MaxEmoji (most frequent emoji) is a critical feature, especially when placed at the end.
- Transformer models show better generalization for emoji-enhanced sarcasm detection.
Future Research Directions
Future research includes further experimentation with Fine-Tuned BERT on emoji-focused sarcasm classification to improve results, and customizing BERT models to capture numerical features. The proposed models can also be used as baselines for sarcasm classification in scenarios lacking situational context or other modalities like visuals/audio. This has potential applications in identifying cyberbullying and hateful content on public and private platforms.
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Estimated Annual Savings
Annual Hours Reclaimed
Your Implementation Roadmap
A structured approach to integrating emoji-centric sarcasm detection into your enterprise workflows.
Data Collection & Pre-processing
Curating and cleaning SarcOji datasets, deriving features like sentiment scores, emoji frequency, and position.
Sarcasm-aware Emoji Embedding Training
Training novel GloVe-based emoji embeddings on SarcOji datasets to capture sarcasm nuances.
Model Development & Fine-tuning
Implementing various ML, ensemble, deep learning classifiers, and fine-tuning BERT models for emoji-focused sarcasm detection.
Performance Evaluation
Testing models on unseen datasets (SarcOjiTest1, SarcOjiTest2) using F1, MCC, and RoC_AuC scores.
Error Analysis & Refinement
Analyzing misclassifications to understand model limitations and refine feature engineering strategies.
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