Sunil Saumya

1.2k total citations · 1 hit paper
34 papers, 796 citations indexed

About

Sunil Saumya is a scholar working on Artificial Intelligence, Sociology and Political Science and Information Systems. According to data from OpenAlex, Sunil Saumya has authored 34 papers receiving a total of 796 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 12 papers in Sociology and Political Science and 8 papers in Information Systems. Recurrent topics in Sunil Saumya's work include Hate Speech and Cyberbullying Detection (17 papers), Sentiment Analysis and Opinion Mining (10 papers) and Spam and Phishing Detection (7 papers). Sunil Saumya is often cited by papers focused on Hate Speech and Cyberbullying Detection (17 papers), Sentiment Analysis and Opinion Mining (10 papers) and Spam and Phishing Detection (7 papers). Sunil Saumya collaborates with scholars based in India, United Kingdom and Saudi Arabia. Sunil Saumya's co-authors include Jyoti Prakash Singh, Yogesh K. Dwivedi, Pradeep Kumar Roy, Nripendra P. Rana, Abhinav Kumar, Arun Chauhan, Abdullah M. Baabdullah, Snehasish Banerjee, Adnan Gutub and V. Lokesha and has published in prestigious journals such as Journal of Business Research, Tribology International and Engineering Applications of Artificial Intelligence.

In The Last Decade

Sunil Saumya

29 papers receiving 743 citations

Hit Papers

Predicting the “helpfulness” of online consumer reviews 2016 2026 2019 2022 2016 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Predicting the “helpfulness” of online consumer reviews
    2016 327 citations Jyoti Prakash Singh, Nripendra P. Rana et al. Journal of Business Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sunil Saumya India 11 495 425 232 121 116 34 796
Radosław Nielek Poland 13 185 0.4× 228 0.5× 170 0.7× 100 0.8× 73 0.6× 40 590
Sören Preibusch United Kingdom 13 205 0.4× 431 1.0× 206 0.9× 84 0.7× 116 1.0× 26 683
Qiongwei Ye China 15 139 0.3× 233 0.5× 94 0.4× 136 1.1× 110 0.9× 43 533
Wolfgang Wörndl Germany 12 108 0.2× 268 0.6× 235 1.0× 117 1.0× 56 0.5× 62 570
Joon Yeon Choeh South Korea 12 224 0.5× 358 0.8× 51 0.2× 145 1.2× 147 1.3× 37 614
Madhu Prabaker United States 7 192 0.4× 247 0.6× 199 0.9× 36 0.3× 62 0.5× 9 566
Faisal Aburub Jordan 10 115 0.2× 152 0.4× 151 0.7× 77 0.6× 150 1.3× 36 515
Rohit Valecha United States 13 139 0.3× 289 0.7× 129 0.6× 32 0.3× 41 0.4× 39 607
Aaron M. French United States 14 69 0.1× 224 0.5× 130 0.6× 72 0.6× 117 1.0× 47 509
Carolyn Brodie United States 12 267 0.5× 385 0.9× 191 0.8× 24 0.2× 71 0.6× 14 560

Countries citing papers authored by Sunil Saumya

Since Specialization
Citations

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

Fields of papers citing papers by Sunil Saumya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sunil Saumya

This figure shows the co-authorship network connecting the top 25 collaborators of Sunil Saumya. A scholar is included among the top collaborators of Sunil Saumya based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sunil Saumya. Sunil Saumya is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Saumya, Sunil, Abhinav Kumar, & Jyoti Prakash Singh. (2024). Filtering offensive language from multilingual social media contents: A deep learning approach. Engineering Applications of Artificial Intelligence. 133. 108159–108159. 7 indexed citations
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Saumya, Sunil, Pradeep Kumar Roy, & Jyoti Prakash Singh. (2023). Review helpfulness prediction on e-commerce websites: A comprehensive survey. Engineering Applications of Artificial Intelligence. 126. 107075–107075. 7 indexed citations
7.
Lokesha, V., et al.. (2023). Intelligent retrieval of wear rate prediction for hypereutectoid steel. Multiscale and Multidisciplinary Modeling Experiments and Design. 6(4). 629–641. 2 indexed citations
8.
Roy, Pradeep Kumar, Sunil Saumya, Jyoti Prakash Singh, Snehasish Banerjee, & Adnan Gutub. (2022). Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review. CAAI Transactions on Intelligence Technology. 8(1). 95–117. 67 indexed citations
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Saumya, Sunil, et al.. (2022). Combating the infodemic: COVID-19 induced fake news recognition in social media networks. Complex & Intelligent Systems. 9(3). 2879–2891. 23 indexed citations
11.
Saumya, Sunil, et al.. (2022). Fighting hate speech from bilingual hinglish speaker’s perspective, a transformer- and translation-based approach.. Social Network Analysis and Mining. 12(1). 87–87. 11 indexed citations
12.
Saumya, Sunil, et al.. (2021). Health Monitoring Using Machine Learning. 2(4). 9–12.
13.
Srivastava, Shivangi, et al.. (2020). NSIT & IIITDWD @ HASOC 2020: Deep learning model for hate-speech identification in Indo-European languages.. 161–167. 7 indexed citations
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Kumar, Abhinav, Sunil Saumya, & Jyoti Prakash Singh. (2020). NITP-AI-NLP@HASOC-FIRE2020: Fine Tuned BERT for the Hate Speech and Offensive Content Identification from Social Media.. 266–273. 3 indexed citations
15.
Kumar, Abhinav, Sunil Saumya, & Jyoti Prakash Singh. (2020). NITP-AI-NLP@UrduFake-FIRE2020: Multi-layer Dense Neural Network for Fake News Detection in Urdu News Articles.. 458–463. 2 indexed citations
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Saumya, Sunil, et al.. (2020). IIIT_DWD@HASOC 2020: Identifying offensive content in Indo-European languages.. 139–144. 3 indexed citations
17.
Kumar, Abhinav, Sunil Saumya, & Jyoti Prakash Singh. (2020). NITP-AI-NLP@HASOC-Dravidian-CodeMix-FIRE2020: A Machine Learning Approach to Identify Offensive Languages from Dravidian Code-Mixed Text.. 384–390. 6 indexed citations
18.
Kumar, Abhinav, Sunil Saumya, & Jyoti Prakash Singh. (2020). NITP-AI-NLP@Dravidian-CodeMix-FIRE2020: A Hybrid CNN and Bi-LSTM Network for Sentiment Analysis of Dravidian Code-Mixed Social Media Posts.. 582–590. 1 indexed citations
19.
Saumya, Sunil & Jyoti Prakash Singh. (2020). Spam review detection using LSTM autoencoder: an unsupervised approach. Electronic Commerce Research. 22(1). 113–133. 34 indexed citations
20.
Singh, Jyoti Prakash, et al.. (2016). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research. 70. 346–355. 327 indexed citations breakdown →

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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