Devamanyu Hazarika
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Recent trends in deep learning based natural language processing
T Young, D Hazarika, S Poria, E Cambria
IEEE Computational Intelligence Magazine 13, 3, 2017
Meld: A multimodal multi-party dataset for emotion recognition in conversations
S Poria, D Hazarika, N Majumder, G Naik, E Cambria, R Mihalcea
arXiv preprint arXiv:1810.02508, 2018
Context-dependent sentiment analysis in user-generated videos
S Poria, E Cambria, D Hazarika, N Majumder, A Zadeh, LP Morency
Proceedings of the 55th annual meeting of the association for computational …, 2017
Dialoguernn: An attentive rnn for emotion detection in conversations
N Majumder, S Poria, D Hazarika, R Mihalcea, A Gelbukh, E Cambria
Proceedings of the AAAI conference on artificial intelligence 33 (01), 6818-6825, 2019
Misa: Modality-invariant and-specific representations for multimodal sentiment analysis
D Hazarika, R Zimmermann, S Poria
Proceedings of the 28th ACM international conference on multimedia, 1122-1131, 2020
SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings
E Cambria, S Poria, D Hazarika, K Kwok
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
Conversational memory network for emotion recognition in dyadic dialogue videos
D Hazarika, S Poria, A Zadeh, E Cambria, LP Morency, R Zimmermann
Proceedings of the conference. Association for Computational Linguistics …, 2018
A deeper look into sarcastic tweets using deep convolutional neural networks
S Poria, E Cambria, D Hazarika, P Vij
Proceedings of COLING 2016, the 26th International Conference on …, 2016
Icon: Interactive conversational memory network for multimodal emotion detection
D Hazarika, S Poria, R Mihalcea, E Cambria, R Zimmermann
Proceedings of the 2018 conference on empirical methods in natural language …, 2018
Multimodal sentiment analysis using hierarchical fusion with context modeling
N Majumder, D Hazarika, A Gelbukh, E Cambria, S Poria
Knowledge-based systems 161, 124-133, 2018
Towards multimodal sarcasm detection (an _obviously_ perfect paper)
S Castro, D Hazarika, V Pérez-Rosas, R Zimmermann, R Mihalcea, ...
arXiv preprint arXiv:1906.01815, 2019
Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research
S Poria, D Hazarika, N Majumder, R Mihalcea
IEEE transactions on affective computing 14 (1), 108-132, 2020
Cascade: Contextual sarcasm detection in online discussion forums
D Hazarika, S Poria, S Gorantla, E Cambria, R Zimmermann, R Mihalcea
arXiv preprint arXiv:1805.06413, 2018
Multimodal sentiment analysis: Addressing key issues and setting up the baselines
S Poria, N Majumder, D Hazarika, E Cambria, A Gelbukh, A Hussain
IEEE Intelligent Systems 33 (6), 17-25, 2018
Multi-level multiple attentions for contextual multimodal sentiment analysis
S Poria, E Cambria, D Hazarika, N Mazumder, A Zadeh, LP Morency
2017 IEEE International Conference on Data Mining (ICDM), 1033-1038, 2017
Conversational transfer learning for emotion recognition
D Hazarika, S Poria, R Zimmermann, R Mihalcea
Information Fusion 65, 1-12, 2021
Multimodal research in vision and language: A review of current and emerging trends
S Uppal, S Bhagat, D Hazarika, N Majumder, S Poria, R Zimmermann, ...
Information Fusion 77, 149-171, 2022
Benchmarking multimodal sentiment analysis
E Cambria, D Hazarika, S Poria, A Hussain, RBV Subramanyam
Computational Linguistics and Intelligent Text Processing: 18th …, 2018
Modeling inter-aspect dependencies for aspect-based sentiment analysis
D Hazarika, S Poria, P Vij, G Krishnamurthy, E Cambria, R Zimmermann
Proceedings of the 2018 Conference of the North American Chapter of the …, 2018
Kingdom: Knowledge-guided domain adaptation for sentiment analysis
D Ghosal, D Hazarika, A Roy, N Majumder, R Mihalcea, S Poria
arXiv preprint arXiv:2005.00791, 2020
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