Tirthankar Ghosal

830 citations
59 papers · 398 · h-index 11

Impact in

Papers in

    • Topic Modeling 40
    • Advanced Text Analysis Techniques 17
    • Sentiment Analysis and Opinion Mining 14
    • Natural Language Processing Techniques 10
    • Software Engineering Research 13
    • Spam and Phishing Detection 4

Tirthankar Ghosal

51 papers receiving 386 citations

Peers

Tirthankar Ghosal
Comparison fields: 5 of 57
  • Artificial Intelligence 298
  • Health Informatics 11
  • Information Systems 163
  • Statistics, Probability and Uncertainty 26
  • Sociology and Political Science 116
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Citations per field
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Citations per year

Countries citing papers authored by Tirthankar Ghosal

Since Specialization
Citations

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

Fields of papers citing papers by Tirthankar Ghosal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202148
2 201934
3 202132
4 202229
5 202218
6 201518
7 202118
8
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection
201810
9 202310
10 202110
11 201910
12 20189
13 20219
14 20229
15 20199
16 20209
17 20238
18 20198
19 20228
20 20218

About Tirthankar Ghosal

Tirthankar Ghosal is a scholar working on Artificial Intelligence, Information Systems, Sociology and Political Science, Molecular Biology and Management Science and Operations Research, having authored 59 papers that have together received 398 indexed citations. Recurring topics across this work include Topic Modeling (40 papers), Advanced Text Analysis Techniques (17 papers), Sentiment Analysis and Opinion Mining (14 papers), Software Engineering Research (13 papers), Natural Language Processing Techniques (10 papers), Misinformation and Its Impacts (8 papers), Biomedical Text Mining and Ontologies (6 papers) and Spam and Phishing Detection (4 papers). The work is most often cited by research in Artificial Intelligence (298 citations), Health Informatics (11 citations), Information Systems (163 citations), Statistics, Probability and Uncertainty (26 citations) and Sociology and Political Science (116 citations). Tirthankar Ghosal has collaborated with scholars based in India, United States and Czechia. Frequent co-authors include Asif Ekbal, Rina Kumari, Pushpak Bhattacharyya, Sandeep Kumar, Sriparna Saha, George Tsatsaronis, Dayne Freitag, Anita de Waard, Valia Kordoni and Song Wang. Their work appears in journals such as International Journal on Digital Libraries, Information Processing & Management, Scientometrics, Scientific Reports and Natural Language 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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