Social networking platforms have become an essential means for communicating feelings to
the entire world due to rapid expansion in the Internet era. Several people use textual …

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 …

Self-attention-based models have achieved remarkable progress in short-text mining.
However, the quadratic computational complexities restrict their application in long text …

Facial expression recognition (FER) has received considerable attention in computer vision,
with" in-the-wild" environments such as human-computer interaction and video …

Emotions are vital for identifying an individual's attitude and mental condition. Detecting and
classifying emotions in Natural Language Processing applications can improve Human …

Researchers have been aware that emotion is not one-hot encoded in emotion-relevant
classification tasks, and multiple emotions can coexist in a given sentence. Recently, several …

Traditional topic models are based on the bag-of-words assumption, which states that the
topic assignment of each word is independent of the others. However, this assumption …

The recent development of deep learning-based natural language processing (NLP)
methods has fostered many downstream applications in various fields. As one of the …

Label distribution learning (LDL) is an effective learning paradigm for dealing with label
ambiguity. When applying LDL, the datasets annotated with label distributions (ie, the real …

Label distribution learning (LDL) is an effective learning paradigm for handling label
ambiguity. When applying LDL, it typically requires datasets annotated with label …