World's Best Scientists 2026 revealed!
Award Badge
Computer Science
Singapore
2026

D-Index & Metrics

Computer Science

D-Index
122
Citations
58989
World Ranking
139
National Ranking
5

Erik Cambria publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Erik Cambria sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 620 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Erik Cambria D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Erik Cambria sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 122 D-Index — 99th percentile

99% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

  • A Survey on Knowledge Graphs: Representation, Acquisition and Applications

    Shaoxiong Ji;Shirui Pan;Erik Cambria;Pekka Marttinen

  • Recent Trends in Deep Learning Based Natural Language Processing

    Tom Young;Devamanyu Hazarika;Soujanya Poria;Erik Cambria

  • Deep Learning--based Text Classification: A Comprehensive Review

    Shervin Minaee;Nal Kalchbrenner;Erik Cambria;Narjes Nikzad

  • New Avenues in Opinion Mining and Sentiment Analysis

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

  • A review of affective computing

    Soujanya Poria;Erik Cambria;Rajiv Bajpai;Amir Hussain

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

    Erik Cambria;Bebo White

  • Affective Computing and Sentiment Analysis

    Erik Cambria

  • Tensor Fusion Network for Multimodal Sentiment Analysis

    Amir Zadeh;Minghai Chen;Soujanya Poria;Erik Cambria

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

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

  • Aspect extraction for opinion mining with a deep convolutional neural network

    Soujanya Poria;Erik Cambria;Alexander Gelbukh

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

    Soujanya Poria;Devamanyu Hazarika;Navonil Majumder;Gautam Naik

  • Context-Dependent Sentiment Analysis in User-Generated Videos.

    Soujanya Poria;Erik Cambria;Devamanyu Hazarika;Navonil Majumder

  • Memory Fusion Network for Multi-view Sequential Learning

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

  • DialogueRNN: An Attentive RNN for Emotion Detection in Conversations.

    Navonil Majumder;Soujanya Poria;Devamanyu Hazarika;Rada Mihalcea

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

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

  • Jumping NLP Curves: A Review of Natural Language Processing Research

    Erik Cambria;Bebo White

  • Representational learning with ELMs for big data

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

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

    Erik Cambria;Soujanya Poria;Devamanyu Hazarika;Kenneth Kwok

  • Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis

    Soujanya Poria;Iti Chaturvedi;Erik Cambria;Amir Hussain

  • Extreme Learning Machine

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

  • Deep Learning-Based Document Modeling for Personality Detection from Text

    Navonil Majumder;Soujanya Poria;Alexander Gelbukh;Erik Cambria

  • Memory Fusion Network for Multi-view Sequential Learning

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

Frequent Co-Authors

Soujanya Poria
Soujanya Poria Nanyang Technological University
Amir Hussain
Amir Hussain Edinburgh Napier University
Alexander Gelbukh
Alexander Gelbukh Instituto Politécnico Nacional
Louis-Philippe Morency
Louis-Philippe Morency Carnegie Mellon University
Luca Oneto
Luca Oneto University of Genoa
Björn Schuller
Björn Schuller Imperial College London
Guang-Bin Huang
Guang-Bin Huang Nanyang Technological University
Rada Mihalcea
Rada Mihalcea University of Michigan–Ann Arbor
Roger Zimmermann
Roger Zimmermann National University of Singapore

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring additional online degrees and career-focused programs can expand your opportunities beyond a traditional computer science track. For those interested in engineering, check out the best online electrical engineering programs USA to discover top-rated programs that blend hands-on skills with technical expertise.

If you prefer a quicker path to employment, consider short certificate programs that pay well. These can help you build relevant, in-demand skills in just a few months and often lead to lucrative roles in tech and related industries.

For those seeking advanced credentials without the traditional time commitment, there are options for the shortest online masters degree programs. Completing a master’s degree faster can give you a competitive edge while saving time and tuition costs.

Unsure which specialization to pursue? Explore the best masters degree to get based on current job market demand and projected growth. This research can help guide your academic and career pathway decisions in technology and beyond.

Best Scientists Citing Erik Cambria

Trending Scientists

Recently Published Articles