60 papers with code • 6 benchmarks • 15 datasets
Given an input, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the writer.
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs.
Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers.
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning
In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets".
Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life.
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.
As opposed to using knowledge from both the modalities separately, we propose a framework to exploit acoustic information in tandem with lexical data.
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.