Hybrid Quantum-Classical Neural Network for Multimodal Multitask Sarcasm, Emotion, and Sentiment Analysis
Bibliographic Data
| ID | 22107668 |
|---|---|
| Authors | Arpan Phukan (0000-0002-9253-1022, Indian Institute of Technology Patna), Santanu Pal (0000-0003-3079-6903, Wipro (India)), Asif Ekbal (0000-0003-3612-8834, Indian Institute of Technology Patna) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 5740-5750 |
| Publication date | 2024-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3388016 |
| OpenAlex | W4396712756 |
| Language | EN |
| Citations received | 3 |
| References cited | 23 |
Sarcasm detection in unimodal or multimodal setting is a very complex task. Sarcasm, emotion, and sentiment are related to each other, and hence any multitask model could be an effective way to leverage the interdependence among these tasks. In order to better represent these clandestine associations, we avoid solely relying on traditional machine learning methods to encode the relationships between the modalities. In this article, we propose a hybrid quantum model that banks upon the low computational complexity and robust representational power of a variational quantum circuit (VQC) and the tried and tested dense neural network to tackle sentiment, emotion, and sarcasm classification simultaneously. We empirically establish that the quantum properties like superposition, entanglement, and interference will better capture and replicate not only the cross-modal interactions between text, acoustics, and visuals but also the correlations between the three responses. We consider the extended MUStARD dataset to evaluate our proposed hybrid model. The results show that our proposed hybrid quantum framework yields more promising results for the primary task of sarcasm detection with the help of the two secondary classification tasks, viz. sentiment and emotion
Artificial neural network · Autoencoder · Discriminative model · ENCODE · Irony · Linguistics · Machine learning · Natural language processing · Sarcasm · Sentiment analysis · Speech recognition · Computer Science · EEG and Brain-Computer Interfaces · Engineering · Machine Learning in Materials Science · Neural Networks and Reservoir Computing · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |