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Computer Science
Mexico
2026

D-Index & Metrics

Computer Science

D-Index
56
Citations
16392
World Ranking
3998
National Ranking
4

Alexander Gelbukh 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 Alexander Gelbukh 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: 547 publications — 95th percentile

95% of scientists in this discipline score the same or lower.

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

Alexander Gelbukh 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 Alexander Gelbukh 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: 56 D-Index — 72nd percentile

72% 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 Mexico Leader Award

Overview

Alexander Gelbukh is affiliated with the Instituto Politécnico Nacional in Mexico and has contributed extensively to the field of Computer Science, with a particular focus on Artificial Intelligence. Their research outputs include significant work in Natural Language Processing and related subfields, reflecting an emphasis on text analysis and computational methods for understanding language.

Their scholarly activity covers a range of topics including Topic Modeling, Natural Language Processing Techniques, Sentiment Analysis and Opinion Mining, Advanced Text Analysis Techniques, Hate Speech and Cyberbullying Detection, Misinformation and Its Impacts, and Spam and Phishing Detection.

Alexander Gelbukh has published research in the following key venues:

  • arXiv (Cornell University)
  • Computación y Sistemas
  • Journal of Intelligent & Fuzzy Systems
  • SSRN Electronic Journal
  • Expert Systems with Applications

Selected recent papers authored or co-authored by Gelbukh include:

  • Multi-label emotion classification in texts using transfer learning, 2022, Expert Systems with Applications
  • Urdu Sentiment Analysis With Deep Learning Methods, 2021, IEEE Access
  • "Bend the truth": Benchmark dataset for fake news detection in Urdu language and its evaluation, 2020, Journal of Intelligent & Fuzzy Systems
  • Threatening Language Detection and Target Identification in Urdu Tweets, 2021, IEEE Access
  • Abusive language detection in youtube comments leveraging replies as conversational context, 2021, PeerJ Computer Science

The scientist has collaborated frequently with other researchers including Grigori Sidorov, Olga Kolesnikova, Sabur Butt, Maaz Amjad, and Fazlourrahman Balouchzahi, with multiple joint publications reflecting an ongoing network of productive research partnerships.

In addition to journal articles, Alexander Gelbukh has contributed to book publications primarily through Springer Science+Business Media. These publications include multiple editions of Computational Linguistics and Intelligent Text Processing released in 2023, as well as Advances in Soft Computing and Advances in Computational Intelligence, both published in 2021.

Best Publications

  • Computational Linguistics and Intelligent Text Processing

    Alexander F. Gelbukh

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

    Soujanya Poria;Erik Cambria;Alexander Gelbukh

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

    Navonil Majumder;Soujanya Poria;Devamanyu Hazarika;Rada Mihalcea

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

    Navonil Majumder;Soujanya Poria;Alexander Gelbukh;Erik Cambria

  • Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis

    Soujanya Poria;Erik Cambria;Alexander Gelbukh

  • DialogueGCN: A graph convolutional neural network for emotion recognition in conversation

    Deepanway Ghosal;Navonil Majumder;Soujanya Poria;Niyati Chhaya

  • Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model

    Grigori Sidorov;Alexander F. Gelbukh;Helena Gómez-Adorno;David Pinto

  • Sentiment Analysis Is a Big Suitcase

    Erik Cambria;Soujanya Poria;Alexander Gelbukh;Mike Thelwall

  • Syntactic N-grams as machine learning features for natural language processing

    Grigori Sidorov;Francisco Velasquez;Efstathios Stamatatos;Alexander Gelbukh

  • Multimodal sentiment analysis using hierarchical fusion with context modeling

    Navonil Majumder;Devamanyu Hazarika;Alexander F. Gelbukh;Erik Cambria

  • A Rule-Based Approach to Aspect Extraction from Product Reviews

    Soujanya Poria;Erik Cambria;Lun-Wei Ku;Chen Gui

  • Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques.

    Kia Dashtipour;Soujanya Poria;Amir Hussain;Erik Cambria

  • Recent trends in deep learning based personality detection

    Yash Mehta;Navonil Majumder;Alexander F. Gelbukh;Erik Cambria

  • Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining

    S. Poria;A. Gelbukh;A. Hussain;N. Howard

  • COSMIC: COmmonSense knowledge for eMotion Identification in Conversations

    Deepanway Ghosal;Navonil Majumder;Alexander F. Gelbukh;Rada Mihalcea

  • Sentiment and Sarcasm Classification With Multitask Learning

    Navonil Majumder;Soujanya Poria;Haiyun Peng;Niyati Chhaya

  • EmoSenticSpace: a novel framework for affective common-sense reasoning

    Soujanya Poria;Alexander Gelbukh;Erik Cambria;Amir Hussain

  • Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines

    Soujanya Poria;Navonil Majumder;Devamanyu Hazarika;Erik Cambria

  • Empirical study of machine learning based approach for opinion mining in tweets

    Grigori Sidorov;Sabino Miranda-Jiménez;Francisco Viveros-Jiménez;Alexander Gelbukh

  • Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns

    Soujanya Poria;Erik Cambria;Alexander Gelbukh;Federica Bisio

  • MIME: MIMicking Emotions for Empathetic Response Generation.

    Navonil Majumder;Pengfei Hong;Shanshan Peng;Jiankun Lu

Frequent Co-Authors

Soujanya Poria
Soujanya Poria Nanyang Technological University
Sivaji Bandyopadhyay
Sivaji Bandyopadhyay Jadavpur University
Erik Cambria
Erik Cambria Nanyang Technological University
Amir Hussain
Amir Hussain Edinburgh Napier University
Manuel Montes-y-Gómez
Manuel Montes-y-Gómez National Institute of Astrophysics, Optics and Electronics
Fabio A. González
Fabio A. González National University of Colombia
Ricardo Baeza-Yates
Ricardo Baeza-Yates Royal Institute of Technology
Rada Mihalcea
Rada Mihalcea University of Michigan–Ann Arbor
Crina Grosan
Crina Grosan King's College London
Efstathios Stamatatos
Efstathios Stamatatos University of the Aegean

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