Erik Cambria

53.8k citations

454 papers · 31.1k indexed · 31 hit papers · h-index 89

Impact in

Artificial Intelligence top 0.01%
- Sentiment Analysis and Opinion Mining
- Topic Modeling
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Natural Language Processing Techniques
Experimental and Cognitive Psychology top 0.1%
- Emotion and Mood Recognition

Papers in

Artificial Intelligence 363
- Sentiment Analysis and Opinion Mining 205
- Topic Modeling 184
- Advanced Text Analysis Techniques 138
- Natural Language Processing Techniques 65
- Text and Document Classification Technologies 47
- Speech and dialogue systems 21
Experimental and Cognitive Psychology 62
- Emotion and Mood Recognition 51

Co-authors: Soujanya Poria Amir Hussain Devamanyu Hazarika Alexander Gelbukh Tom Young Bebo White Louis–Philippe Morency Shaoxiong Ji
Journals: Cognitive Computation (34 papers)Information Fusion (34 papers)IEEE Intelligent Systems (22 papers)Knowledge-Based Systems (20 papers)Neurocomputing (17 papers)
Partner nations: Singapore China United Kingdom

In The Last Decade

Erik Cambria

430 papers receiving 30.0k citations

Hit Papers

15 papers align trajectories log scale

A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics 2025 · 41 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2025 Information Fusion
2023 IEEE Intelligent Systems
2022 IEEE Transactions on Affective Computing
2022 Information Fusion
2021 Neurocomputing
2021 ACM Computing Surveys
2021 Knowledge-Based Systems
2021 IEEE Transactions on Neural Networks and Learning Systems
2020 SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis
2020 Future Generation Computer Systems
2019 Expert Systems with Applications
2018 IEEE Computational Intelligence Magazine
2018 Proceedings of the AAAI Conference on Artificial Intelligence
2018 PubMed
2018 Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
2018 ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
2018 Proceedings of the AAAI Conference on Artificial Intelligence
2017 IEEE Intelligent Systems
2017 Context-Dependent Sentiment Analysis in User-Generated Videos
2017 IEEE Intelligent Systems
2017 Artificial Intelligence Review
2017 Information Fusion
2016 Knowledge-Based Systems
2016 Edinburgh Napier Research Repository (Edinburgh Napier University)
2016 IEEE Intelligent Systems
2015 Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis
2015 Neurocomputing
2014 Jumping NLP Curves: A Review of Natural Language Processing Research
2014 Proceedings of the AAAI Conference on Artificial Intelligence
2014 IEEE Computational Intelligence Magazine
2013 IEEE Intelligent Systems

Peers

Countries citing papers authored by Erik Cambria

Since Specialization

Citations

This map shows the geographic impact of Erik Cambria's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Erik Cambria with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erik Cambria more than expected).

Fields of papers citing papers by Erik Cambria

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erik Cambria. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Erik Cambria. The network helps show where Erik Cambria may publish in the future.

Co-authors

The 25 scholars most cited alongside Erik Cambria, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Erik Cambria Line = papers co-authored together Erik Cambria links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work
1	Ueco: Unified editing chain for efficient appearance transfer with multimodality-guided diffusion Expert Systems with Applications ·Yuansheng Ma, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou	2025	1
2	BForTFin: A Financial Domain-Aware Multiscale Evaluation Method for Time-Series Foundation Models Nigel Cheong, Ling Wei Hsuen, Ranjan Satapathy, Erik Cambria, Rick Siow Mong Goh, Joyjit Chattoraj	2025	0
3	MER 2025: When Affective Computing Meets Large Language Models DR-NTU (Nanyang Technological University) ·Zheng Lian, Rui Liu, Kele Xu, Bin Liu, Xuefei Liu, Yazhou Zhang, Xin Liu, Yong Li, Zebang Cheng, Haolin Zuo, Ziyang Ma, Xiong Peng, Xie Chen, Ya Li, Erik Cambria, Guoying Zhao, Björn W. Schuller, Jianhua Tao	2025	0
4	Explainable natural language processing for corporate sustainability analysis Information Fusion ·Keane Ong, Rui Mao, Ranjan Satapathy, Ricardo Shirota Filho, Erik Cambria, Johan Sulaeman, Gianmarco Mengaldo	2024	7
5	MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition Zheng Lian, Haiyang Sun, Licai Sun, Zhuofan Wen, Siyuan Zhang, Shun Chen, Hao Gu, Jinming Zhao, Ziyang Ma, Xie Chen, Jiangyan Yi, Rui Liu, Kele Xu, Bin Liu, Erik Cambria, Guoying Zhao, Björn W. Schuller, Jianhua Tao	2024	4
6	SemEval 2024 - Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF) arXiv (Cornell University) ·Shivani Kumar, Md Shad Akhtar, Erik Cambria, Tanmoy Chakraborty	2024	2
7	How Interpretable are Reasoning Explanations from Prompting Large Language Models? Yeo Wei Jie, Ranjan Satapathy, Rick Siow Mong Goh, Erik Cambria	2024	11
8	Understanding Natural Language Understanding Erik Cambria	2024	14
9	A survey on semantic processing techniques Information Fusion ·Rui Mao, Kai He, Xulang Zhang, Guanyi Chen, Jinjie Ni, Zonglin Yang, Erik Cambria	2023	24
10	Teaching Simulations Supported by Artificial Intelligence in the Real World Education Sciences ·Iti Chaturvedi, Erik Cambria, Roy E. Welsch	2023	4
11	MEGACare: Knowledge-guided multi-view hypergraph predictive framework for healthcare Information Fusion ·Jialun Wu, Kai He, Rui Mao, Chen Li, Erik Cambria	2023	27
12	Emotion-and-knowledge grounded response generation in an open-domain dialogue setting Knowledge-Based Systems ·Deeksha Varshney, Asif Ekbal, Erik Cambria	2023	7
13	Marshall–Olkin power-law distributions in length-frequency of entities Knowledge-Based Systems ·Xiaoshi Zhong, Xiang Yu, Erik Cambria, Jagath C. Rajapakse	2023	1
14	SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition Proceedings of the AAAI Conference on Artificial Intelligence ·Wei Li, Luyao Zhu, Rui Mao, Erik Cambria	2023	49
15	MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) ·Zheng Lian, Haiyang Sun, Licai Sun, Kang Chen, Mngyu Xu, Kexin Wang, Ke Xu, Yu He, Ying Li, Jinming Zhao, Ye Liu, Bin Liu, Jiangyan Yi, Meng Wang, Erik Cambria, Guoying Zhao, Björn W. Schuller, Jianhua Tao	2023	28
16	A survey on computational metaphor processing techniques: from identification, interpretation, generation to application Artificial Intelligence Review ·Mengshi Ge, Rui Mao, Erik Cambria	2023	11
17	Meta-Based Self-Training and Re-Weighting for Aspect-Based Sentiment Analysis IEEE Transactions on Affective Computing ·Kai He, Rui Mao, Tieliang Gong, Chen Li, Erik Cambria	2022	67
18	Semantic matching in machine reading comprehension: An empirical study Information Processing & Management ·Qian Liu, Rui Mao, Xiubo Geng, Erik Cambria	2022	24
19	Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets National University of Singapore ·Frank Xing, Lorenzo Malandri, Yue Zhang, Erik Cambria	2020	42
20	Feature ensemble plus sample selection: domain adaptation for sentiment classification Queensland's institutional digital repository (The University of Queensland) ·Rui Xia, Chengqing Zong, Xuelei Hu, Erik Cambria	2013	3

About Erik Cambria

Erik Cambria is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Computational Mathematics, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 454 papers that have together received 31.1k indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (205 papers), Topic Modeling (184 papers), Advanced Text Analysis Techniques (138 papers), Natural Language Processing Techniques (65 papers), Emotion and Mood Recognition (51 papers), Text and Document Classification Technologies (47 papers), Stock Market Forecasting Methods (33 papers) and Speech and dialogue systems (21 papers). The work is most often cited by research in Artificial Intelligence (23.1k citations), Experimental and Cognitive Psychology (4.4k citations), Management Science and Operations Research (2.3k citations), Computer Vision and Pattern Recognition (3.6k citations) and Signal Processing (1.8k citations). Erik Cambria has collaborated with scholars based in Singapore, China and United Kingdom. Frequent co-authors include Soujanya Poria, Amir Hussain, Devamanyu Hazarika, Alexander Gelbukh, Tom Young, Bebo White, Louis–Philippe Morency, Shaoxiong Ji, Catherine Havasi and Björn W. Schuller. Their work appears in journals such as Cognitive Computation, Information Fusion, IEEE Intelligent Systems, Knowledge-Based Systems and Neurocomputing.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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