相关论文: Improving Language Models for Emotion Analysis: In…
Training emotion recognition models has relied heavily on human annotated data, which present diversity, quality, and cost challenges. In this paper, we explore the potential of Large Language Models (LLMs), specifically GPT4, in automating…
In recent decades, the field of affective computing has made substantial progress in advancing the ability of AI systems to recognize and express affective phenomena, such as affect and emotions, during human-human and human-machine…
Emotions can provide a natural communication modality to complement the existing multi-modal capabilities of social robots, such as text and speech, in many domains. We conducted three online studies with 112, 223, and 151 participants to…
Subjective and sentiment analysis have gained considerable attention recently. Most of the resources and systems built so far are done for English. The need for designing systems for other languages is increasing. This paper surveys…
Conversations emerge as the primary media for exchanging ideas and conceptions. From the listener's perspective, identifying various affective qualities, such as sarcasm, humour, and emotions, is paramount for comprehending the true…
Many Natural Language Processing works on emotion analysis only focus on simple emotion classification without exploring the potentials of putting emotion into "event context", and ignore the analysis of emotion-related events. One main…
As emotion plays a growing role in robotic research it is crucial to develop methods to analyze and compare among the wide range of approaches. To this end we present a survey of 1427 IEEE and ACM publications that include robotics and…
Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by…
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a…
Understanding the cognitive and emotional perceptions of people who commit suicide is one of the most sensitive scientific challenges. There are circumstances where people feel the need to leave something written, an artifact where they…
Training a model with access to human explanations can improve data efficiency and model performance on in- and out-of-domain data. Adding to these empirical findings, similarity with the process of human learning makes learning from…
We present an approach utilizing Topological Data Analysis to study the structure of face poses used in affective computing, i.e., the process of recognizing human emotion. The approach uses a conditional comparison of different emotions,…
Neural Machine Translation (NMT) is the task of translating a text from one language to another with the use of a trained neural network. Several existing works aim at incorporating external information into NMT models to improve or control…
In the past years, sentiment analysis has increasingly shifted attention to representational frameworks more expressive than semantic polarity (being positive, negative or neutral). However, these richer formats (like Basic Emotions or…
Text emotion detection constitutes a crucial foundation for advancing artificial intelligence from basic comprehension to the exploration of emotional reasoning. Most existing emotion detection datasets rely on manual annotations, which are…
The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in…
Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding and generation. Advanced utilization of the knowledge embedded in LLMs for automated annotation has consistently been explored. This study…
The research project aims to apply an integrated approach to natural language processing NLP to satisfaction surveys. It will focus on understanding and extracting relevant information from survey responses, analyzing feelings, and…
Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the…
This document presents endeavors to represent emotion in a computational cognitive architecture. The first part introduces research organizing with two axes of emotional affect: pleasantness and arousal. Following this basic of emotional…