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 …

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 …

Despite its importance, the time variable has been largely neglected in the NLP and
language model literature. In this paper, we present TimeLMs, a set of language models …

The experimental landscape in natural language processing for social media is too
fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics …

Despite the recent success of multi-task learning and transfer learning for natural language
processing (NLP), few works have systematically studied the effect of scaling up the number …

Language models are ubiquitous in current NLP, and their multilingual capacity has recently
attracted considerable attention. However, current analyses have almost exclusively focused …

Pre-trained language models (PLMs) are fundamental for natural language processing
applications. Most existing PLMs are not tailored to the noisy user-generated text on social …

Pretrained language models (PTLMs) are typically learned over a large, static corpus and
further fine-tuned for various downstream tasks. However, when deployed in the real world …

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 …

Research on neural networks has focused on understanding a single model trained on a
single dataset. However, relatively little is known about the relationships between different …