Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and
analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the …

Fine-tuning pre-trained language models has become the prevalent paradigm for building
downstream NLP models. Oftentimes fine-tuned models are readily available but their …

Several datasets have been annotated and published for classification of emotions. They
differ in several ways:(1) the use of different annotation schemata (eg, discrete label sets …

Most research on emotion analysis from text focuses on the task of emotion classification or
emotion intensity regression. Fewer works address emotions as a phenomenon to be …

The recognition of hate speech and offensive language (HOF) is commonly formulated as a
classification task to decide if a text contains HOF. We investigate whether HOF detection …

In the field of sentiment analysis, several studies have highlighted that a single sentence
may express multiple, sometimes contrasting, sentiments and emotions, each with its own …

Urdu is a widely used language in South Asia and worldwide. While there are similar
datasets available in English, we created the first multi-label emotion dataset consisting of …

Past shared tasks on emotions use data with both overt expressions of emotions (I am so
happy to see you!) as well as subtle expressions where the emotions have to be inferred, for …

While fine-tuning pretrained models has become common practice, these models often
underperform outside their specific domains. Recently developed model merging …

Emotions are a vital and fundamental part of our existence. Whatever we do, say, or do not
say somehow reflects our feelings, however not immediately. To comprehend human's most …