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
Barawi, M., Lin, C., Siddharthan, A. and Liu, Y. (2019) Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics, pp. (In Press)
Wibowo, A., Siddharthan, A., Masthoff, J. and Lin, C. (2018) Understanding how to Explain Package Recommendations in the Clothes Domain, Workshop: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS) at 12th ACM Conference on Recommender Systems (RecSys), Vancouver, Canada
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