Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model
Abstract
:1. Introduction
1.1. Challenges in Sarcasm Detection
1.2. Major Contribution
- Creating a reliable and effective hybrid autoencoder-based model to detect sarcasm on social sites. The model employs a LSTM-based autoencoder for further learning from the results.
- The novelty lies in combining hybrid models from sentence-based embeddings and unsupervised learning utilizing the autoencoder to overcome their limitations. The models are selected by using ablation method and selecting those models which can cover the diverse range of real-time datasets.
- The model is tested against publicly available real-world social media datasets such as Twitter and Reddit. Validation on the real-world datasets is true measure of the metrics for the model. The models can be employed directly over real-world social media contents and can handle diverse contents.
- The model’s application is universal and can be used on diverse social media platforms. Datasets have diversified short tweets from Twitter, short sentence from Reddit, and long sentences from newspaper lines.
2. Related Work
Current Issues
3. Proposed Model
3.1. AutoEncoder
3.2. Universal Sentence Encoder (USE)
3.3. BERT
4. Experimental Results
4.1. Datasets
4.1.1. Twitter Dataset
4.1.2. SARC Dataset
4.1.3. Headline Dataset
4.2. Performance Measure
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | SARC | Headlines | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Models | Acc. | Prec. | Recall | MCC | Acc. | Prec. | Recall | MCC | Acc. | Prec. | Recall | MCC |
Auto-Encoder | 80.91% | 0.81 | 0.8 | 0.66 | 89.75% | 0.9 | 0.88 | 0.8 | 91.89% | 0.92 | 0.89 | 0.85 |
USE + Auto-Encoder | 82.46% | 0.82 | 0.82 | 0.67 | 89.90% | 0.91 | 0.86 | 0.81 | 92.32% | 0.93 | 0.92 | 0.85 |
BERT + Auto-Encoder | 82.42% | 0.83 | 0.81 | 0.67 | 90.35% | 0.91 | 0.88 | 0.81 | 92.40% | 0.94 | 0.91 | 0.85 |
USE + BERT + Auto-Encoder (Proposed) | 83.92% | 0.83 | 0.85 | 0.68 | 90.81% | 0.92 | 0.912 | 0.81 | 92.80% | 0.95 | 0.91 | 0.86 |
Models | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|
CASCADE [36] | 74.00% | - | - | 0.75 |
SARC [18] | 77.00% | - | - | - |
Elmo-BiLSTM [37] | 79.00% | - | - | - |
RCNN-RoBERTa [29] | 79.00% | 0.78 | 0.78 | 0.78 |
Multi-Head Attn [26] | 81.00% | - | - | - |
Proposed Model | 83.92% | 0.83 | 0.85 | 0.84 |
Models | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|
Sarcasm Magnet [21] | - | 0.90 | 0.89 | 0.90 |
Sentence-level Attn [22] | 74.90% | 0.749 | 0.75 | 749 |
Self Matching Netwk [23] | 74.40% | 0.763 | 0.725 | 0.744 |
A2Text-Net [24] | 80.10% | 0.83 | 0.802 | 0.801 |
Multi-Head Attn [26] | 81.20% | 0.809 | 0.818 | 0.812 |
Proposed Model | 92.80% | 0.95 | 0.91 | 0.93 |
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Sharma, D.K.; Singh, B.; Agarwal, S.; Kim, H.; Sharma, R. Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model. Electronics 2022, 11, 2844. https://doi.org/10.3390/electronics11182844
Sharma DK, Singh B, Agarwal S, Kim H, Sharma R. Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model. Electronics. 2022; 11(18):2844. https://doi.org/10.3390/electronics11182844
Chicago/Turabian StyleSharma, Dilip Kumar, Bhuvanesh Singh, Saurabh Agarwal, Hyunsung Kim, and Raj Sharma. 2022. "Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model" Electronics 11, no. 18: 2844. https://doi.org/10.3390/electronics11182844