|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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, understanding consumer feedback in live conversations, etc.
Ranked #2 on Emotion Recognition in Conversation on SEMAINE
Emotion recognition in conversations is crucial for building empathetic machines.
Ranked #3 on Emotion Recognition in Conversation on SEMAINE
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
Ranked #1 on Multimodal Emotion Recognition on Monologue
Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers.
The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour.
In this work, we adopt a feature-engineering based approach to tackle the task of speech emotion recognition.
Ranked #1 on Speech Emotion Recognition on IEMOCAP
Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions.
Ranked #2 on Multimodal Sentiment Analysis on CMU-MOSEI