Emotion recognition has recently attracted extensive interest due to its significant
applications to human–computer interaction. The expression of human emotion depends on …

The omnipresence of numerous information sources in our daily life brings up new
alternatives for emotion recognition in several domains including e-health, e-learning …

Because of the rapid growth of mobile technology, social media has become an essential
platform for people to express their views and opinions. Understanding public opinion can …

Understanding emotion expressed in language has a wide range of applications, from
building empathetic chatbots to detecting harmful online behavior. Advancement in this area …

Abstract Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …

Over the last decade Deep learning-based models surpasses classical machine learning
models in a variety of text classification tasks. The primary challenge with text classification …

Deep learning has emerged as a powerful machine learning technique that learns multiple
layers of representations or features of the data and produces state‐of‐the‐art prediction …

In this paper, we present the SemEval-2019 Task 3-EmoContext: Contextual Emotion
Detection in Text. Lack of facial expressions and voice modulations make detecting …

Emotions are expressed in nuanced ways, which varies by collective or individual
experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed …

Abstract Big Data and Deep Learning algorithms combined with enormous computing power
have paved ways for significant technological advancements. Technology is evolving to …