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DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Navonil Majumder, Soujanya Poria, Devamanyu Hazarika +3
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining…
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder +3
Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi…
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling
N. Majumder, D. Hazarika, A. Gelbukh +2
Multimodal sentiment analysis is a very actively growing field of research. A promising area of opportunity in this field is to improve the multimodal fusion mechanism. We present…
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla +3
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text. However, a sarcastic sentence can be expressed with cont…
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
Soujanya Poria, Navonil Majumder, Devamanyu Hazarika +3
We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning based architectures for multimodal sentim…