Seng-Beng Ho

784 total citations
25 papers, 409 citations indexed

About

Seng-Beng Ho is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Seng-Beng Ho has authored 25 papers receiving a total of 409 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Management Science and Operations Research. Recurrent topics in Seng-Beng Ho's work include AI-based Problem Solving and Planning (7 papers), Sentiment Analysis and Opinion Mining (6 papers) and Advanced Text Analysis Techniques (6 papers). Seng-Beng Ho is often cited by papers focused on AI-based Problem Solving and Planning (7 papers), Sentiment Analysis and Opinion Mining (6 papers) and Advanced Text Analysis Techniques (6 papers). Seng-Beng Ho collaborates with scholars based in Singapore and China. Seng-Beng Ho's co-authors include Erik Cambria, Zhaoxia Wang, Zhaoxia Wang, Melvin Chen, Rui Mao, Zhenda Hu, Zhiping Lin, Ah‐Hwee Tan, Haibo Pen and Guitao Cao and has published in prestigious journals such as Neural Computing and Applications, Artificial Intelligence Review and Multimedia Tools and Applications.

In The Last Decade

Seng-Beng Ho

21 papers receiving 392 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seng-Beng Ho Singapore	8	266	71	68	42	39	25	409
Luyao Zhu China	11	292 1.1×	47 0.7×	73 1.1×	44 1.0×	48 1.2×	21	415
Ranjan Satapathy Singapore	11	315 1.2×	53 0.7×	32 0.5×	56 1.3×	28 0.7×	25	420
Marcin Gruza Poland	8	405 1.5×	25 0.4×	20 0.3×	48 1.1×	62 1.6×	13	581
Arkadiusz Janz Poland	7	297 1.1×	19 0.3×	21 0.3×	36 0.9×	36 0.9×	19	465
Kamil Kanclerz Poland	8	359 1.3×	20 0.3×	21 0.3×	41 1.0×	46 1.2×	11	529
Julita Bielaniewicz Poland	6	295 1.1×	17 0.2×	20 0.3×	36 0.9×	36 0.9×	8	458
Yash Mehta United Kingdom	4	134 0.5×	69 1.0×	16 0.2×	30 0.7×	24 0.6×	6	318
Fazal Masud Kundi Pakistan	13	435 1.6×	23 0.3×	67 1.0×	136 3.2×	83 2.1×	16	541
Bartłomiej Koptyra Poland	3	246 0.9×	14 0.2×	20 0.3×	32 0.8×	30 0.8×	5	403
Mateusz Kochanek Poland	3	246 0.9×	13 0.2×	21 0.3×	31 0.7×	32 0.8×	3	404

Countries citing papers authored by Seng-Beng Ho

Since Specialization

Citations

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

Fields of papers citing papers by Seng-Beng Ho

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seng-Beng Ho. 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 Seng-Beng Ho. The network helps show where Seng-Beng Ho may publish in the future.

Co-authorship network of co-authors of Seng-Beng Ho

This figure shows the co-authorship network connecting the top 25 collaborators of Seng-Beng Ho. A scholar is included among the top collaborators of Seng-Beng Ho 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 Seng-Beng Ho. Seng-Beng Ho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Zhaoxia, et al.. (2025). A review of Chinese sentiment analysis: subjects, methods, and trends. Artificial Intelligence Review. 58(3). 7 indexed citations

Jiang, Jing, et al.. (2024). Converting Vocal Performances into Sheet Music Leveraging Large Language Models. 445–452.

Wang, Zhaoxia, et al.. (2024). Joint Weakly Supervised Image Emotion Analysis Based on Interclass Discrimination and Intraclass Correlation. IEEE Intelligent Systems. 39(5). 82–89. 2 indexed citations

Wang, Zhaoxia, et al.. (2023). Learning-Based Stock Trending Prediction by Incorporating Technical Indicators and Social Media Sentiment. Cognitive Computation. 15(3). 1092–1102. 25 indexed citations

Wang, Zhaoxia, et al.. (2023). Survey on sentiment analysis: evolution of research methods and topics. Artificial Intelligence Review. 56(8). 8469–8510. 76 indexed citations

Wang, Zhaoxia, Zhenda Hu, Seng-Beng Ho, Erik Cambria, & Ah‐Hwee Tan. (2023). MiMuSA—mimicking human language understanding for fine-grained multi-class sentiment analysis. Neural Computing and Applications. 35(21). 15907–15921. 13 indexed citations

Wang, Zhaoxia, et al.. (2023). Knowledge Graph enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge. 791–798. 3 indexed citations

Cambria, Erik, Rui Mao, Melvin Chen, Zhaoxia Wang, & Seng-Beng Ho. (2023). Seven Pillars for the Future of Artificial Intelligence. IEEE Intelligent Systems. 38(6). 62–69. 57 indexed citations

Hu, Zhenda, Zhaoxia Wang, Seng-Beng Ho, & Ah‐Hwee Tan. (2021). Stock Market Trend Forecasting Based on Multiple Textual Features: A Deep Learning Method. 1002–1007. 5 indexed citations

10.

Ho, Seng-Beng, Mark Edmonds, & Song‐Chun Zhu. (2020). Actional-Perceptual Causality: Concepts and Inductive Learning for AI and Robotics. 3. 442–448. 1 indexed citations

11.

Wang, Zhaoxia, Seng-Beng Ho, & Erik Cambria. (2020). A review of emotion sensing: categorization models and algorithms. Multimedia Tools and Applications. 79(47-48). 35553–35582. 95 indexed citations

12.

Ho, Seng-Beng, et al.. (2018). Learning Correlations and Causalities Through an Inductive Bootstrapping Process. 7. 2270–2277. 2 indexed citations

13.

Wang, Zhaoxia, Seng-Beng Ho, & Zhiping Lin. (2018). Stock Market Prediction Analysis by Incorporating Social and News Opinion and Sentiment. 1375–1380. 24 indexed citations

14.

Ho, Seng-Beng, et al.. (2017). On Inductive Learning of Causal Knowledge for Problem Solving.. National Conference on Artificial Intelligence. 4 indexed citations

15.

Ho, Seng-Beng. (2017). Causal Learning versus Reinforcement Learning for Knowledge Learning and Problem Solving.. National Conference on Artificial Intelligence. 2 indexed citations

16.

Ho, Seng-Beng. (2017). The Role of Synchronic Causal Conditions in Visual Knowledge Learning. 21. 9–16. 4 indexed citations

17.

Ho, Seng-Beng. (2016). Deep thinking and quick learning for viable AI. 156–164. 5 indexed citations

18.

Ho, Seng-Beng. (2013). Operational Representation — A Unifying Representation for Activity Learning and Problem Solving. National University of Singapore.

19.

Ho, Seng-Beng, et al.. (2013). Incremental Rule Chunking for Problem Solving. 323–328.

20.

Iyer, Laxmi & Seng-Beng Ho. (2013). Perception and prediction — A connectionist model. National University of Singapore. 25–32. 1 indexed citations

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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