2026 Erik Cambria: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	122	139	134	5	5	620	58989

Research.com Recognitions

2026 - Research.com Computer Science in Singapore Leader Award
2025 - Research.com Computer Science in Singapore Leader Award
2023 - Research.com Computer Science in Singapore Leader Award
2022 - Research.com Computer Science in Singapore Leader Award

Overview

Erik Cambria is affiliated with Nanyang Technological University in Singapore and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence. Their research output includes 598 publications primarily situated within computer science, with significant emphasis on artificial intelligence, experimental and cognitive psychology, and computer vision and pattern recognition.

Their work addresses diverse topics including topic modeling, sentiment analysis and opinion mining, advanced text analysis techniques, natural language processing techniques, emotion and mood recognition, text and document classification technologies, and mental health via writing.

Among their recent papers are:

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2021, IEEE Transactions on Neural Networks and Learning Systems)
Deep Learning--based Text Classification (2021, ACM Computing Surveys)
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for Sentiment Analysis (2020, Future Generation Computer Systems)
Multimodal Sentiment Analysis: A Systematic Review of History, Datasets, Multimodal Fusion Methods, Applications, Challenges and Future Directions (2022, Information Fusion)
Aspect-based Sentiment Analysis via Affective Knowledge Enhanced Graph Convolutional Networks (2021, Knowledge-Based Systems)

The scientist collaborates frequently with other researchers such as Rui Mao, Amir Hussain, Björn W. Schuller, Ranjan Satapathy, and Kai He. These collaborative efforts reflect a multidisciplinary approach within their research community.

Erik Cambria has been published predominantly in venues including:

arXiv (Cornell University)
Information Fusion
Cognitive Computation
IEEE Intelligent Systems
Artificial Intelligence Review

Their published book titled Time Expression and Named Entity Recognition was released in 2021 by Springer International Publishing.

Best Publications

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Tom Young;Devamanyu Hazarika;Soujanya Poria;Erik Cambria

2886
Citations
A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Shaoxiong Ji;Shirui Pan;Erik Cambria;Pekka Marttinen

2431
Citations
Recent Trends in Deep Learning Based Natural Language Processing

Tom Young;Devamanyu Hazarika;Soujanya Poria;Erik Cambria

1753
Citations
Deep Learning--based Text Classification: A Comprehensive Review

Shervin Minaee;Nal Kalchbrenner;Erik Cambria;Narjes Nikzad

1665
Citations
New Avenues in Opinion Mining and Sentiment Analysis

E. Cambria;B. Schuller;Yunqing Xia;C. Havasi

1651
Citations
A review of affective computing

Soujanya Poria;Erik Cambria;Rajiv Bajpai;Amir Hussain

1618
Citations
Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

Erik Cambria;Bebo White

1421
Citations
Affective Computing and Sentiment Analysis

Erik Cambria

1414
Citations
Tensor Fusion Network for Multimodal Sentiment Analysis

Amir Zadeh;Minghai Chen;Soujanya Poria;Erik Cambria

1340
Citations
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

AmirAli Bagher Zadeh;Paul Pu Liang;Soujanya Poria;Erik Cambria

1071
Citations
Aspect extraction for opinion mining with a deep convolutional neural network

Soujanya Poria;Erik Cambria;Alexander Gelbukh

934
Citations
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Soujanya Poria;Devamanyu Hazarika;Navonil Majumder;Gautam Naik

880
Citations
Context-Dependent Sentiment Analysis in User-Generated Videos.

Soujanya Poria;Erik Cambria;Devamanyu Hazarika;Navonil Majumder

837
Citations
Memory Fusion Network for Multi-view Sequential Learning

Amir Zadeh;Paul Pu Liang;Navonil Mazumder;Soujanya Poria

773
Citations
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations.

Navonil Majumder;Soujanya Poria;Devamanyu Hazarika;Rada Mihalcea

689
Citations
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis

Mohammad Ehsan Basiri;Shahla Nemati;Moloud Abdar;Erik Cambria

684
Citations
Jumping NLP Curves: A Review of Natural Language Processing Research

Erik Cambria;Bebo White

669
Citations
Representational learning with ELMs for big data

Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou;Guang-Bin Huang;Chi Man Vong

656
Citations
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings

Erik Cambria;Soujanya Poria;Devamanyu Hazarika;Kenneth Kwok

656
Citations
Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis

Soujanya Poria;Iti Chaturvedi;Erik Cambria;Amir Hussain

633
Citations
Extreme Learning Machine

Erik Cambria;Guang-Bin Huang;Liyanaarachchi Lekamalage Chamara Kasun;Hongming Zhou

620
Citations
Deep Learning-Based Document Modeling for Personality Detection from Text

Navonil Majumder;Soujanya Poria;Alexander Gelbukh;Erik Cambria

620
Citations
Memory Fusion Network for Multi-view Sequential Learning

Amir Zadeh;Paul Pu Liang;Navonil Mazumder;Soujanya Poria

598
Citations

Frequent Co-Authors

Soujanya Poria Nanyang Technological University

Amir Hussain Edinburgh Napier University

Alexander Gelbukh Instituto Politécnico Nacional

Roy E. Welsch MIT

Louis-Philippe Morency Carnegie Mellon University

Luca Oneto University of Genoa

Björn Schuller Imperial College London

Guang-Bin Huang Nanyang Technological University

Rada Mihalcea University of Michigan–Ann Arbor

Roger Zimmermann National University of Singapore

External Links

Personal website of Erik Cambria Google Scholar page

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