Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
As a Japanese graphic symbol widely used in the world, Emoji plays an important role in computer mediated communication. Despite its prevalent use, the interaction dynamics between emoji and textual sentences remain inadequately explored. Based on the emotional function of emoji, this study uses the indirect priming method to explore the emotional impact of emoji on subsequent text in computer mediated communication through two progressive behavioral experiments. The results show that: (1) Emoji positioned at the onset of a sentence induce an emotional priming effect; (2) The processing speed is slowest when emoji and text are emotionally conflicting, while in non-conflicting condition, the type of emoji moderates the processing of combined sentences; (3) The emotional influence of emoji plays an auxiliary role, and the valence of textual sentence plays a decisive role in emotional perception.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11238005 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2024.e32984 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Communicative Social Intentions Modulate Emotional Mimicry Responses.
Psychophysiology
September 2025
Department of Psychology, Clinical Psychology and Psychotherapy, University of Regensburg, Regensburg, Germany.
Facial emotional expressions are interactive signals that communicate intentions. Previous research has shown that sending a facial emotional expression influences the evaluation of response expressions, but the mechanisms behind this effect remain unclear. In a preregistered experiment, 68 participants were asked to send an emoji (happy, neutral, and angry) to a virtual agent in front of them, whereupon the agent reacted with either a smiling or frowning facial expression.
View Article and Find Full Text PDFMachine Learning and Lexical Rule-Based Cost-Efficient Emotion Annotation of Hinglish Utterances.
J Vis Exp
August 2025
Chitkara University Institute of Engineering & Technology, Chitkara University.
Emotion annotation in code-mixed languages like Hinglish (Hindi-English) presents unique challenges due to linguistic complexity and resource constraints. This study introduces a hybrid active learning framework that combines lexical rules, machine learning, and iterative expert feedback to achieve cost-efficient, high-accuracy emotion annotation. Grounded in psychological theories of emotion, including Discrete Emotions Theory and Cognitive Appraisal Theory, the framework employs bilingual emotion dictionaries (e.
View Article and Find Full Text PDFEffectiveness of the mood shifter ball intervention on stress and burnout among nurses.
J Educ Health Promot
July 2025
Department of Nursing Foundation, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India.
Background: Occupational stress and burnout are prevalent among nurses due to heavy workload, extended shifts, and inadequate staffing, that have a negative impact on their well-being and patient care. Effective interventions are crucial to address these challenges. The study aimed to assess the effectiveness of the mood shifter ball intervention on stress and burnout among nurses at a tertiary care hospital in Chennai and to extrapolate themes from reflective practices.
View Article and Find Full Text PDFArab2Vec: An Arabic word embedding model for use in Twitter NLP applications.
PLoS One
August 2025
Biocomputing and Developmental Systems (BDS) Research Group, University of Limerick, Limerick, Ireland.
The analysis of Arabic Twitter data sets is a highly active research topic, particularly since the outbreak of COVID-19 and subsequent attempts to understand public sentiment related to the pandemic. This activity is partially driven by the high number of Arabic Twitter users, around 164 million. Word embedding models are a vital tool for analysing Twitter data sets, as they are considered one of the essential methods of transforming words into numbers that can be processed using machine learning (ML) algorithms.
View Article and Find Full Text PDFDevelopment and performance of a generative pretrained transformer for diabetes care.
Diabetes Res Clin Pract
September 2025
Red Cross Nursing University Center, Adhered to the University of Seville, Spain; Department of Nursing. Faculty of Nursing, Physiotherapy and Podiatry, University of Seville, Spain.
Aims: To design and evaluate the performance of a diabetes-related Generative Pretrained Transformer (GPT).
Methods: A prompt-engineered layer over GPT was developed in four stages: (1) literature review on GPT tools development; (2) selection and preprocessing of 65 information sources about diabetes care strategies, patient education, diabetes technologies, and cultural care, among others; (3) prototype development; and (4) final tool evaluation using 420 diabetes-related questions adapted from three validated instruments. Outcomes were accuracy, rationale, citations, disclaimers, and emoji exclusion.