Erik Cambria

Associate Professor

Nanyang Technological University (NTU)

Singapore

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Erik Cambria is an Associate Professor at Nanyang Technological University in Singapore. His research areas include emotion recognition in conversations, multilingual reputation generation, and bias in AI models, as reflected in his recent publications. Cambria actively investigates applications of sentiment analysis, multimodal AI, and logical reasoning in large language models.

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Collaborators (4)

Yulan He

Professor

King’s College London

UNITED KINGDOM

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Frank Xing

Assistant Professor

National University of Singapore (NUS)

SINGAPORE

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Przemysław Kazienko

POLAND

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Jagath C. Rajapakse

Nanyang Technological University (NTU)

SINGAPORE

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Erik Cambria

Research Interests

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About

Articles (29)

A Literature Survey on Multimodal and Multilingual Sexism Detection

Learning chain for clause awareness: Triplex-contrastive learning for emotion recognition in conversations

Periodic insight: Multilingual reputation generation system through daily opinion mining analysis

Gender Bias in Text-to-Video Generation Models: A Case Study of Sora

Knowing What and Why: Causal emotion entailment for emotion recognition in conversations

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

A Retrieval-Augmented Multiagent System for Financial Sentiment Analysis

A review of Chinese sentiment analysis: subjects, methods, and trends

A Comparative Analysis of Metaphorical Cognition in ChatGPT and Human Minds

A client-server based recognition system: Non-contact single/multiple emotional and behavioral state assessment methods

A survey on semantic processing techniques

Financial Sentiment Analysis: Techniques and Applications

XTime: A general rule-based method for time expression recognition and normalization

AdaCLF: An Adaptive Curriculum Learning Framework for Emotional Support Conversation

Recent Developments in Recommender Systems: A Survey [Review Article]

Toward Responsible Recommender Systems

Self-supervised utterance order prediction for emotion recognition in conversations

Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?

Emotion-and-knowledge grounded response generation in an open-domain dialogue setting

A Wide Evaluation of ChatGPT on Affective Computing Tasks

KnowleNet: Knowledge fusion network for multimodal sarcasm detection

MEGACare: Knowledge-guided multi-view hypergraph predictive framework for healthcare

Marshall–Olkin power-law distributions in length-frequency of entities

Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis

Enhancing Arabic‐text feature extraction utilizing label‐semantic augmentation in few/zero‐shot learning

Time expression recognition and normalization: a survey

Time expression recognition and normalization: a survey

Survey on sentiment analysis: evolution of research methods and topics

AutoML-Emo: Automatic Knowledge Selection Using Congruent Effect for Emotion Identification in Conversations

Collaborators (4)

Yulan He

Frank Xing

Przemysław Kazienko

Jagath C. Rajapakse

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