Pawan Goyal

3.3k citations
120 papers · 1.5k · h-index 20

Impact in

    • Public Relations and Crisis Communication
    • Topic Modeling
    • Natural Language Processing Techniques
    • Advanced Text Analysis Techniques
    • Sentiment Analysis and Opinion Mining
    • Hate Speech and Cyberbullying Detection

Papers in

    • Topic Modeling 58
    • Natural Language Processing Techniques 49
    • Advanced Text Analysis Techniques 19
    • Sentiment Analysis and Opinion Mining 10
    • Text and Document Classification Technologies 9
    • Hate Speech and Cyberbullying Detection 7
    • Expert finding and Q&A systems 8

Pawan Goyal

110 papers receiving 1.4k citations

Peers

Pawan Goyal
Comparison fields: 5 of 116
  • Communication 265
  • Artificial Intelligence 945
  • Health Informatics 20
  • Statistics, Probability and Uncertainty 99
  • Statistical and Nonlinear Physics 163
Replace Jana Diesner with:
Jana Diesner United States
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Citations per field
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Citations per year

Countries citing papers authored by Pawan Goyal

Since Specialization
Citations

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

Fields of papers citing papers by Pawan Goyal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Pawan Goyal, 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 Pawan Goyal Line = papers co-authored together Pawan Goyal links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015165
2 2015124
3 201988
4 201881
5 202052
6 201849
7 201446
8 201445
9 201645
10 202235
11 202135
12 201935
13 201235
14 202134
15 201529
16 201427
17 202227
18 201925
19 201522
20 202321

About Pawan Goyal

Pawan Goyal is a scholar working on Artificial Intelligence, Information Systems, Communication, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics, having authored 120 papers that have together received 1.5k indexed citations. Recurring topics across this work include Topic Modeling (58 papers), Natural Language Processing Techniques (49 papers), Advanced Text Analysis Techniques (19 papers), Sentiment Analysis and Opinion Mining (10 papers), Complex Network Analysis Techniques (9 papers), Text and Document Classification Technologies (9 papers), Expert finding and Q&A systems (8 papers) and Hate Speech and Cyberbullying Detection (7 papers). The work is most often cited by research in Communication (265 citations), Artificial Intelligence (945 citations), Health Informatics (20 citations), Statistics, Probability and Uncertainty (99 citations) and Statistical and Nonlinear Physics (163 citations). Pawan Goyal has collaborated with scholars based in India, United States and Germany. Frequent co-authors include Niloy Ganguly, Animesh Mukherjee, Saptarshi Ghosh, Koustav Rudra, Siddhartha Jonnalagadda, Mark D. Huffman, Binny Mathew, Prasenjit Mitra, Punyajoy Saha and Muhammad Imran. Their work appears in journals such as Language Resources and Evaluation, Proceedings of the ACM on Human-Computer Interaction, IEEE Transactions on Computational Social Systems, Expert Systems with Applications and IEEE Transactions on Knowledge and Data Engineering.

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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