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Devamanyu Hazarika
Researcher at National University of Singapore
Publications - 61
Citations - 8068
Devamanyu Hazarika is an academic researcher from National University of Singapore. The author has contributed to research in topics: Sentiment analysis & Computer science. The author has an hindex of 22, co-authored 50 publications receiving 5038 citations. Previous affiliations of Devamanyu Hazarika include Singapore University of Technology and Design & National Institute of Technology, Warangal.
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Journal ArticleDOI
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
TL;DR: This paper reviews significant deep learning related models and methods that have been employed for numerous NLP tasks and provides a walk-through of their evolution.
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Recent Trends in Deep Learning Based Natural Language Processing
TL;DR: Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains as mentioned in this paper, such as natural language processing (NLP).
Proceedings ArticleDOI
Context-Dependent Sentiment Analysis in User-Generated Videos.
Soujanya Poria,Erik Cambria,Devamanyu Hazarika,Navonil Majumder,Amir Zadeh,Louis-Philippe Morency +5 more
TL;DR: A LSTM-based model is proposed that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process and showing 5-10% performance improvement over the state of the art and high robustness to generalizability.
Proceedings ArticleDOI
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
TL;DR: The Multimodal EmotionLines Dataset (MELD) as discussed by the authors is a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue.
Journal ArticleDOI
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations.
Navonil Majumder,Soujanya Poria,Devamanyu Hazarika,Rada Mihalcea,Alexander Gelbukh,Erik Cambria +5 more
TL;DR: A new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification and outperforms the state of the art by a significant margin on two different datasets.