Mayur Wankhade
Impact in
- Artificial Intelligence top 2%
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Topic Modeling
- Text and Document Classification Technologies
- Natural Language Processing Techniques
Papers in
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- Sentiment Analysis and Opinion Mining 8
- Advanced Text Analysis Techniques 6
- Topic Modeling 5
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- Spam and Phishing Detection 3
- Technology and Data Analysis 1
- Co-authors
- Annavarapu Chandra Sekhara Rao (5 shared papers)Chaitanya Kulkarni (2 shared papers)Chandra Sekhara Rao Annavarapu (3 shared papers)Ajith Abraham (2 shared papers)Binod Kumar Singh (1 shared paper)Baijnath Kaushik (1 shared paper)
- Journals
- Multimedia Tools and Applications (1 paper)Applied Soft Computing (1 paper)International Journal of Multimedia Information Retrieval (1 paper)Artificial Intelligence Review (1 paper)Scientific Reports (1 paper)
- Partner nations
- IndiaUnited States
In The Last Decade
Mayur Wankhade
9 papers receiving 724 citations
Mayur Wankhade's Hit Papers
Peers
Comparison fields: 5 of 99
- Artificial Intelligence 530
- Health Informatics 9
- General Social Sciences 21
- Information Systems 118
- Computer Science Applications 22
Countries citing papers authored by Mayur Wankhade
This map shows the geographic impact of Mayur Wankhade'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 Mayur Wankhade with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mayur Wankhade more than expected).
Fields of papers citing papers by Mayur Wankhade
This network shows the impact of papers produced by Mayur Wankhade. 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 Mayur Wankhade. The network helps show where Mayur Wankhade may publish in the future.
Co-authors
The 6 scholars most cited alongside Mayur Wankhade, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A survey on sentiment analysis methods, applications, and challenges Hit paper breakdown → | 2022 | 681 |
| 2 | 2023 | 19 | |
| 3 | 2023 | 14 | |
| 4 | 2021 | 13 | |
| 5 | 2022 | 11 | |
| 6 | 2023 | 10 | |
| 7 | 2024 | 10 | |
| 8 | 2017 | 6 | |
| 9 | 2022 | 3 |
About Mayur Wankhade
Mayur Wankhade is a scholar working on Artificial Intelligence, Information Systems, Sociology and Political Science, Public Health, Environmental and Occupational Health and Cultural Studies, having authored 9 papers that have together received 767 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (8 papers), Advanced Text Analysis Techniques (6 papers), Topic Modeling (5 papers), Spam and Phishing Detection (3 papers), Technology and Data Analysis (1 paper), Diverse Approaches in Healthcare and Education Studies (1 paper), Diverse Topics in Contemporary Research (1 paper) and Misinformation and Its Impacts (1 paper). The work is most often cited by research in Artificial Intelligence (530 citations), Health Informatics (9 citations), General Social Sciences (21 citations), Information Systems (118 citations) and Computer Science Applications (22 citations). Mayur Wankhade has collaborated with scholars based in India and United States. Frequent co-authors include Annavarapu Chandra Sekhara Rao, Chaitanya Kulkarni, Chandra Sekhara Rao Annavarapu, Ajith Abraham, Binod Kumar Singh and Baijnath Kaushik. Their work appears in journals such as Multimedia Tools and Applications, Applied Soft Computing, International Journal of Multimedia Information Retrieval, Artificial Intelligence Review and Scientific Reports.
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.