Jeff Da

435 citations
6 papers · 247 · h-index 5

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Advanced Graph Neural Networks
    • Advanced Text Analysis Techniques
    • Speech and dialogue systems
    • Sentiment Analysis and Opinion Mining
    • Explainable Artificial Intelligence (XAI)
    • Multimodal Machine Learning Applications

Papers in

    • Topic Modeling 5
    • Natural Language Processing Techniques 4
    • Advanced Graph Neural Networks 2
    • Sentiment Analysis and Opinion Mining 1
    • Advanced Text Analysis Techniques 1
    • Humor Studies and Applications 1

Jeff Da

5 papers receiving 235 citations

Peers

Jeff Da
Comparison fields: 5 of 35
  • Artificial Intelligence 219
  • Computer Vision and Pattern Recognition 61
  • General Social Sciences 6
  • Management Science and Operations Research 13
  • Experimental and Cognitive Psychology 12
Replace Aaron Steven White with:
Aaron Steven White United States
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Silviu Paun United Kingdom
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Jeff Da relative to Aaron Steven White United States Aaron Steven White's profile →
Citations per field
00.5×1.7×
Aaron Steven White · 1×
Citations per year

Countries citing papers authored by Jeff Da

Since Specialization
Citations

This map shows the geographic impact of Jeff Da'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 Jeff Da with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Da more than expected).

Fields of papers citing papers by Jeff Da

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeff Da. 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 Jeff Da. The network helps show where Jeff Da may publish in the future.

Co-authors

The 17 scholars most cited alongside Jeff Da, 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 Jeff Da Line = papers co-authored together Jeff Da links everyone, so they are left out of the graph.

All Works

About Jeff Da

Jeff Da is a scholar working on Artificial Intelligence, Social Psychology, Sociology and Political Science, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 6 papers that have together received 247 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (4 papers), Advanced Graph Neural Networks (2 papers), Sentiment Analysis and Opinion Mining (1 paper), Language, Metaphor, and Cognition (1 paper), Misinformation and Its Impacts (1 paper), Advanced Text Analysis Techniques (1 paper) and Humor Studies and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (219 citations), Computer Vision and Pattern Recognition (61 citations), General Social Sciences (6 citations), Management Science and Operations Research (13 citations) and Experimental and Cognitive Psychology (12 citations). Jeff Da has collaborated with scholars based in United States. Frequent co-authors include Yejin Choi, Jena D. Hwang, Antoine Bosselut, Ronan Le Bras, Chandra Bhagavatula, Keisuke Sakaguchi, Jungo Kasai, Rowan Zellers, Kyle Lo and Ari Holtzman.

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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