World's Best Scientists 2026 revealed!
Donghong Ji

Donghong Ji

D-Index & Metrics

Computer Science

D-Index
32
Citations
4333
World Ranking
13179
National Ranking
1611

Donghong Ji 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 Donghong Ji 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: 165 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Donghong Ji 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 Donghong Ji 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Donghong Ji is affiliated with Wuhan University in China and has made extensive contributions to the field of computer science, with a focus on artificial intelligence and related subfields.

The scientist's work spans several main topics, including:

  • Topic Modeling
  • Sentiment Analysis and Opinion Mining
  • Natural Language Processing Techniques
  • Advanced Text Analysis Techniques
  • Text and Document Classification Technologies
  • Text Readability and Simplification
  • Speech and dialogue systems

Donghong Ji's research is concentrated primarily within computer science, with significant publications in artificial intelligence. Other subfields covered by their work include computer vision and pattern recognition, sociology and political science, information systems, and experimental and cognitive psychology.

Key recent publications include:

  • "Latent Emotion Memory for Multi-Label Emotion Classification," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training," 2022, ACM Transactions on Information Systems
  • "Topic-Enhanced Capsule Network for Multi-Label Emotion Classification," 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Emoji-Based Sentiment Analysis Using Attention Networks," 2020, ACM Transactions on Asian and Low-Resource Language Information Processing

They have published extensively in the following venues:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • ACM Transactions on Asian and Low-Resource Language Information Processing
  • Neurocomputing

Frequent collaborators include Hao Fei, Fei Li, Bobo Li, Chong Teng, and Yafeng Ren, with coauthorship counts ranging from 17 to 37 publications.

Best Publications

  • The CHEMDNER corpus of chemicals and drugs and its annotation principles.

    Martin Krallinger;Obdulia Rabal;Florian Leitner;Miguel Vazquez

  • Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification.

    Hao Tang;Donghong Ji;Chenliang Li;Qiji Zhou

  • Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information

    GuoDong Zhou;Min Zhang;DongHong Ji;QiaoMing Zhu

  • A neural joint model for entity and relation extraction from biomedical text

    Fei Li;Meishan Zhang;Guohong Fu;Donghong Ji

  • Neural networks for deceptive opinion spam detection

    Yafeng Ren;Donghong Ji

  • Context-sensitive Twitter sentiment classification using neural network

    Yafeng Ren;Yue Zhang;Meishan Zhang;Donghong Ji

  • A topic-enhanced word embedding for Twitter sentiment classification

    Yafeng Ren;Ruimin Wang;Donghong Ji

  • Relation Extraction Using Label Propagation Based Semi-Supervised Learning

    Jinxiu Chen;Donghong Ji;Chew Lim Tan;Zhengyu Niu

  • Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning

    Zheng-Yu Niu;Dong-Hong Ji;Chew Lim Tan

  • Long short-term memory RNN for biomedical named entity recognition

    Chen Lyu;Bo Chen;Yafeng Ren;Donghong Ji

  • Latent Emotion Memory for Multi-Label Emotion Classification

    Hao Fei;Yue Zhang;Yafeng Ren;Donghong Ji

  • Boundaries and edges rethinking: An end-to-end neural model for overlapping entity relation extraction

    Hao Fei;Yafeng Ren;Donghong Ji

  • Enriching contextualized language model from knowledge graph for biomedical information extraction.

    Hao Fei;Yafeng Ren;Yue Zhang;Donghong Ji

  • Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge

    Shengqiong Wu;Hao Fei;Yafeng Ren;Donghong Ji

  • Positive Unlabeled Learning for Deceptive Reviews Detection

    yafeng ren;donghong ji;hongbin zhang

  • Unsupervised Feature Selection for Relation Extraction

    Jinxiu Chen;Donghong Ji;Chew Lim Tan;Zhengyu Niu

  • Towards Twitter sentiment classification by multi-level sentiment-enriched word embeddings

    Shufeng Xiong;Hailian Lv;Weiting Zhao;Donghong Ji

  • Improving Twitter sentiment classification using topic-enriched multi-prototype word embeddings

    Yafeng Ren;Yue Zhang;Meishan Zhang;Donghong Ji

  • On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

    Unknown

  • A short text sentiment-topic model for product reviews

    Shufeng Xiong;Shufeng Xiong;Kuiyi Wang;Donghong Ji;Bingkun Wang

  • Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus

    Hao Fei;Meishan Zhang;Donghong Ji

  • Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax

    Hao Fei;Fei Li;Bobo Li;Donghong Ji

  • Learning to Detect Deceptive Opinion Spam: A Survey

    Unknown

  • Overview of the NTCIR-7 ACLIA Tasks: Advanced Cross-Lingual Information Access

    Teruko Mitamura;Eric Nyberg;Hideki Shima;Tsuneaki Kato

  • HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations

    Jingye Li;Donghong Ji;Fei Li;Meishan Zhang

  • Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling

    Unknown

Frequent Co-Authors

Hao Fei
Hao Fei National University of Singapore
Yue Zhang
Yue Zhang Westlake University
Chew Lim Tan
Chew Lim Tan National University of Singapore
Meishan Zhang
Meishan Zhang Harbin Institute of Technology
Guodong Zhou
Guodong Zhou Soochow University
Min Zhang
Min Zhang Tsinghua University
Tetsuya Sakai
Tetsuya Sakai Waseda University
Wenjie Li
Wenjie Li Hong Kong Polytechnic University
Teruko Mitamura
Teruko Mitamura Carnegie Mellon University
Eric Nyberg
Eric Nyberg Carnegie Mellon University

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