Erik Cambria
Datos Biográficos
| ID | 6537089 |
|---|---|
| NOMBRE | Erik Cambria |
| NOMBRES | Erik |
| APELLIDO | Cambria |
| FIRMA | CAMBRIA E |
| AFILIACIONES | Nanyang Technological University |
| ORCID | 0000-0002-3030-1280 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 9 |
| TOTAL DE CITAS | 0 |
| OBRAS COMO AUTOR | 9 |
| OBRAS COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2018 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
SenticNet 9
PBDD
Automated detection of depressive moods from social media holds great promise for early mental health intervention, yet existing multimodal approaches typically require large quantities of annotated data and extensive feature engineering, impeding their deployment in real‐world settings where labels are scarce. To address this challenge, we propose prompt‐based depression detection (PBDD), a novel prompt‐based few‐shot learning framework that lev…
Cime
Multimodal emotion analysis is pivotal in decoding complex human affect by integrating diverse data sources such as text, audio, and visual signals. In this article, we introduce contextual interaction-based multimodal emotion analysis with enhanced semantic information (CIME), a novel spatio-temporal interaction network that significantly improves emotion recognition accuracy and robustness. CIME employs a text-centric cross-modal attention mech…
Transfer Learning for Cross-Domain Sentiment Classification via Domain Adaptation
The exponential proliferation of user-generated content across online platforms has driven a substantial surge in domain-specific textual data. Consequently, sentiment analysis has emerged to facilitate context-aware, real-time decision making in dynamic, data-intensive domains. Nevertheless, obtaining large-scale annotated data for all domains remains impractical, therefore a necessity for robust cross-domain sentiment classification techniques …
A Literature Survey on Multimodal and Multilingual Sexism Detection
Sexism has become a pressing issue, driven by the rapid-spreading influence of societal norms, media portrayals, and online platforms that perpetuate and amplify gender biases. Curbing sexism has emerged as a critical challenge globally. Being capable of recognizing sexist statements and behaviors is of particular importance since it is the first step in mind change. This survey provides an extensive overview of recent advancements in sexism dete…
Ensemble Hybrid Learning Methods for Automated Depression Detection
Changes in human lifestyle have led to an increase in the number of people suffering from depression over the past century. Although in recent years, rates of diagnosing mental illness have improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding of depression detection requires effective feature representation and analysis of language use. In this article, text class…
Teaching Simulations Supported by Artificial Intelligence in the Real World
Video conferencing has enabled synchronous communication in a classroom and created multi-sensory content to stimulate learners. Artificial intelligence involves complex equations that are better taught using a constructive pedagogy where students experiment with alternative ways of solving the same problem. Multiple-choice questions have high reliability and can easily reveal student skill levels in a quick way. The Australian Computer Society a…
Suicidal Ideation Detection
Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is…
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution…
Sin obras prominentes en esta página.
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution…
Suicidal Ideation Detection
Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is…
Ensemble Hybrid Learning Methods for Automated Depression Detection
Changes in human lifestyle have led to an increase in the number of people suffering from depression over the past century. Although in recent years, rates of diagnosing mental illness have improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding of depression detection requires effective feature representation and analysis of language use. In this article, text class…
Teaching Simulations Supported by Artificial Intelligence in the Real World
Video conferencing has enabled synchronous communication in a classroom and created multi-sensory content to stimulate learners. Artificial intelligence involves complex equations that are better taught using a constructive pedagogy where students experiment with alternative ways of solving the same problem. Multiple-choice questions have high reliability and can easily reveal student skill levels in a quick way. The Australian Computer Society a…
Transfer Learning for Cross-Domain Sentiment Classification via Domain Adaptation
The exponential proliferation of user-generated content across online platforms has driven a substantial surge in domain-specific textual data. Consequently, sentiment analysis has emerged to facilitate context-aware, real-time decision making in dynamic, data-intensive domains. Nevertheless, obtaining large-scale annotated data for all domains remains impractical, therefore a necessity for robust cross-domain sentiment classification techniques …
A Literature Survey on Multimodal and Multilingual Sexism Detection
Sexism has become a pressing issue, driven by the rapid-spreading influence of societal norms, media portrayals, and online platforms that perpetuate and amplify gender biases. Curbing sexism has emerged as a critical challenge globally. Being capable of recognizing sexist statements and behaviors is of particular importance since it is the first step in mind change. This survey provides an extensive overview of recent advancements in sexism dete…
SenticNet 9
PBDD
Automated detection of depressive moods from social media holds great promise for early mental health intervention, yet existing multimodal approaches typically require large quantities of annotated data and extensive feature engineering, impeding their deployment in real‐world settings where labels are scarce. To address this challenge, we propose prompt‐based depression detection (PBDD), a novel prompt‐based few‐shot learning framework that lev…
Cime
Multimodal emotion analysis is pivotal in decoding complex human affect by integrating diverse data sources such as text, audio, and visual signals. In this article, we introduce contextual interaction-based multimodal emotion analysis with enhanced semantic information (CIME), a novel spatio-temporal interaction network that significantly improves emotion recognition accuracy and robustness. CIME employs a text-centric cross-modal attention mech…
Computer Science (6 obras) · Sentiment Analysis and Opinion Mining (5 obras) · Artificial Intelligence (4 obras) · Mental Health via Writing (4 obras) · Deep learning (3 obras) · Digital Mental Health Interventions (3 obras) · Machine learning (3 obras) · Natural language processing (3 obras) · Psychology (3 obras) · Computer security (2 obras)