Peer-reviewed Journal Publications
- Lim, Sunghoon, and Conrad S. Tucker. "Mining Twitter data for causal links between tweets and real-world outcomes." Expert Systems with Applications: X 3 (2019): 100007.
- Lim, Sunghoon, Conrad S. Tucker, Kathryn Jablokow, and Bart Pursel. "A semantic network model for measuring engagement and performance in online learning platforms." Computer Applications in Engineering Education 26, no. 5 (2018): 1481-1492.
- Tuarob, Suppawong, Sunghoon Lim, and Conrad S. Tucker. "Automated Discovery of Product Feature Inferences within Large Scale Implicit Social Media Data." Journal of Computing and Information Science in Engineering 18, no. 2 (2018): 021017.
- Lim, Sunghoon, and Conrad S. Tucker. "Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers." Journal of Mechanical Design 139, no. 11 (2017): 111409.
- Lim, Sunghoon, Conrad S. Tucker, and Soundar Kumara. "An unsupervised machine learning model for discovering latent infectious diseases using social media data." Journal of Biomedical Informatics 66 (2017): 82-94. (PubMed)
- Lim, Sunghoon, and Conrad S. Tucker. "A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data." Journal of Mechanical Design 138, no. 6 (2016): 061403.
Peer-reviewed Conference Proceedings
- Lim, Sunghoon, Conrad S. Tucker, Kathryn Jablokow, and Bart Pursel. "Quantifying the Mismatch between Course Content and Students’ Dialogue in Online Learning Environments." In ASME 2017 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2017. (DEC Technical Committee Best Paper Award consisting of a prize of $1,000)
- Baek, DaeSeon, and Sunghoon Lim. “Smart farming: Developing growth programs and reforming environmental conditions for hog raising using machine vision and deep learning.” 2019 KIIE/KORMS/KSS Joint Spring Conference, Gwangju, Republic of Korea, 2019.