Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

We cannot overemphasize the essence of contextual information in most natural language
processing (NLP) applications. The extraction of context yields significant improvements in …

Thanks to the breakthrough of large-scale pre-trained language model (PLM) technology,
prompt-based classification tasks, eg, sentiment analysis and emotion detection, have …

Emotion identification based on multimodal data (eg, audio, video, text, etc.) is one of the
most demanding and important research fields, with various uses. In this context, this …

With the constantly growing number of reviews and other sentiment-bearing texts on the
Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect …

Along with the emergence of the Internet, the rapid development of handheld devices has
democratized content creation due to the extensive use of social media and has resulted in …

Artificial Intelligence (AI) has been used for processing data to make decisions, Interact with
humans, and understand their feelings and emotions. With the advent of the Internet, people …

Affective computing is an emerging interdisciplinary research field bringing together
researchers and practitioners from various fields, ranging from artificial intelligence, natural …

Understanding emotions is one of the most important aspects of personal development and
growth and, as such, it is a key tile for the emulation of human intelligence. Besides being a …

Emotion recognition in text is an important natural language processing (NLP) task whose
solution can benefit several applications in different fields, including data mining, e-learning …