Chenghua LinResearch Associate
I joined KMi as research associate in Oct 2011 after receiving my PhD in computing from the University of Exeter. My PhD study focused on developing statistical topic models for sentiment analysis from the Web.
My current research effort is mainly devoted to developing theoretically principled machine learning models for social media analysis and semantic Web applications.
Keys: Sentiment analysis, Machine learning, Probabilistic Bayesian modelling, natural language processing, Text mining
Wibowo, A., Siddharthan, A., Masthoff, J. and Lin, C. (2018) Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation, Proceedings of the 26th ACM Conference on User Modelling, Adaptation and Personalization (UMAP)
Wibowo, A., Siddharthan, A., Lin, C. and Masthoff, J. (2017) Matrix Factorization for Package Recommendations, Workshop: Workshop on Recommendation in Complex Scenarios (ComplexRec 2017) at RecSys, Como, Italy
Barawi, M., Lin, C. and Siddharthan, A. (2017) Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels, International Conference on Applications of Natural Language to Information Systems Lecture Notes in Computer Science, eds. Frasincar F., Ittoo A., Nguyen L., M�tais E, 10260, pp. 299-312, Springer, Cham
He, Y., Lin, C., Gao, W. and Wong, K. (2012) Dynamic Joint Sentiment-Topic Model, ACM Transactions on Intelligent Systems and Technology, In Press
Lin, C., He, Y., Pedrinaci, C. and Domingue, J. (2012) Feature LDA: a Supervised Topic Model for Automatic Detection of Web API Documentations from the Web, The 11th International Semantic Web Conference (ISWC), Boston, USA