Rui Mao

2.9k total citations · 2 hit papers
103 papers, 1.6k citations indexed

About

Rui Mao is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology and Management Science and Operations Research. According to data from OpenAlex, Rui Mao has authored 103 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Artificial Intelligence, 19 papers in Experimental and Cognitive Psychology and 14 papers in Management Science and Operations Research. Recurrent topics in Rui Mao's work include Topic Modeling (30 papers), Natural Language Processing Techniques (24 papers) and Sentiment Analysis and Opinion Mining (18 papers). Rui Mao is often cited by papers focused on Topic Modeling (30 papers), Natural Language Processing Techniques (24 papers) and Sentiment Analysis and Opinion Mining (18 papers). Rui Mao collaborates with scholars based in Singapore, China and United Kingdom. Rui Mao's co-authors include Erik Cambria, Kai He, Chenghua Lin, Frank Guérin, Wei Li, Qika Lin, Qian Liu, Xiao Li, Chen Li and Tieliang Gong and has published in prestigious journals such as Journal of Medicinal Chemistry, Cerebral Cortex and Expert Systems with Applications.

In The Last Decade

Rui Mao

92 papers receiving 1.6k citations

Hit Papers

The Biases of Pre-Trained Language Models: An Empirical S... 2022 2026 2023 2024 2022 2025 40 80 120
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. The Biases of Pre-Trained Language Models: An Empirical Study on Prompt-Based Sentiment Analysis and Emotion Detection
    2022 148 citations Rui Mao, Qian Liu et al. IEEE Transactions on Affective Computing profile →
  2. A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics
    2025 41 citations Kai He, Rui Mao et al. Information Fusion profile →

Peers

Rui Mao
Yukun Ma Singapore
Liang-Chih Yu Taiwan
Rebecca J. Passonneau United States
Bo Xu China
Yingyu Liang United States
Jean Y. Wu United States
Chenghua Lin United Kingdom
Hans van Ditmarsch France
Khurshid Ahmad United Kingdom
Yukun Ma Singapore
Rui Mao
Citations per year, relative to Rui Mao Rui Mao (= 1×) peers Yukun Ma

Countries citing papers authored by Rui Mao

Since Specialization
Citations

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

Fields of papers citing papers by Rui Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rui Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Rui Mao. A scholar is included among the top collaborators of Rui Mao 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 Rui Mao. Rui Mao 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
1.
Mao, Rui, et al.. (2025). FinMR: A Knowledge-Intensive Multimodal Benchmark for Advanced Financial Reasoning. ResearchSpace (University of Auckland). 168–176.
2.
Xiao, Luwei, Rui Mao, Shuai Zhao, et al.. (2025). Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis. IEEE Transactions on Affective Computing. 16(4). 3248–3265. 4 indexed citations
3.
4.
Mao, Rui, et al.. (2024). Medical Report Generation via Multimodal Spatio-Temporal Fusion. 4699–4708. 6 indexed citations
5.
Zhao, Yuanyuan, et al.. (2024). Natural language processing in finance: A survey. Information Fusion. 115. 102755–102755. 12 indexed citations
6.
Mao, Rui, et al.. (2023). A survey on semantic processing techniques. Information Fusion. 101. 101988–101988. 24 indexed citations
7.
Wu, Jialun, et al.. (2023). MEGACare: Knowledge-guided multi-view hypergraph predictive framework for healthcare. Information Fusion. 100. 101939–101939. 27 indexed citations
8.
Mao, Rui, et al.. (2023). A survey on computational metaphor processing techniques: from identification, interpretation, generation to application. Artificial Intelligence Review. 56(S2). 1829–1895. 11 indexed citations
9.
Li, Jiangnan, Xinxin Zou, Rui Mao, et al.. (2023). Determination of the role of hippocampal astrocytes in the bilateral common carotid artery stenosis mouse model by RNA sequencing. Neuroscience Letters. 805. 137213–137213. 4 indexed citations
10.
Li, Wei, Luyao Zhu, Rui Mao, & Erik Cambria. (2023). SKIER: A Symbolic Knowledge Integrated Model for Conversational Emotion Recognition. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13121–13129. 49 indexed citations
11.
He, Kai, et al.. (2023). Template-Free Prompting for Few-Shot Named Entity Recognition via Semantic-Enhanced Contrastive Learning. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 18357–18369. 17 indexed citations
12.
Zhu, Luyao, Wei Li, Rui Mao, Vlad Pandelea, & Erik Cambria. (2023). PAED: Zero-Shot Persona Attribute Extraction in Dialogues. 9771–9787. 18 indexed citations
13.
Liu, Qian, Rui Mao, Xiubo Geng, & Erik Cambria. (2022). Semantic matching in machine reading comprehension: An empirical study. Information Processing & Management. 60(2). 103145–103145. 24 indexed citations
14.
He, Kai, Rui Mao, Tieliang Gong, Chen Li, & Erik Cambria. (2022). Meta-Based Self-Training and Re-Weighting for Aspect-Based Sentiment Analysis. IEEE Transactions on Affective Computing. 14(3). 1731–1742. 67 indexed citations
15.
He, Kai, et al.. (2022). Virtual prompt pre-training for prototype-based few-shot relation extraction. Expert Systems with Applications. 213. 118927–118927. 46 indexed citations
16.
Mao, Rui, Qian Liu, Kai He, Wei Li, & Erik Cambria. (2022). The Biases of Pre-Trained Language Models: An Empirical Study on Prompt-Based Sentiment Analysis and Emotion Detection. IEEE Transactions on Affective Computing. 14(3). 1743–1753. 148 indexed citations breakdown →
17.
Qiao, Shaojie, Nan Han, Faliang Huang, et al.. (2021). LMNNB: Two-in-One imbalanced classification approach by combining metric learning and ensemble learning. Applied Intelligence. 52(7). 7870–7889. 7 indexed citations
18.
Qiao, Shaojie, et al.. (2020). A novel handover detection model via frequent trajectory patterns mining. International Journal of Machine Learning and Cybernetics. 11(12). 2587–2606. 3 indexed citations
19.
Qiao, Shaojie, et al.. (2020). SOTB: Semi-Supervised Oversampling Approach Based on Trigonal Barycenter Theory. IEEE Access. 8. 50180–50189. 4 indexed citations
20.
Mao, Rui. (2010). An Analysis of Some Problems on the Studies of "Left Behind Children". 2 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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