Aspect-based Opinion Mining from Product Reviews

Bio | Summary


Samaneh Moghaddam is a PhD candidate in the school of computer science at Simon Fraser University. Her main research interests include information retrieval, text mining, machine learning, natural language processing, and social network analysis. Samaneh is a senior member of SFU Database and Data mining lab under supervision of Dr. Martin Ester. Her PhD thesis is &ldqup;Aspect-based Opinion Mining from Online Product Reviews&rdqup;. She has proposed some novel techniques for extracting product aspects and estimating their ratings from product reviews. Samaneh Moghaddam has published several papers in top international conferences such as ACM CIKM, ACM SIGIR, and WSDM. Her publications are mostly related to her research on information retrieval and machine learning models for mining opinion from product reviews.

Martin Ester received a PhD in Computer Science from ETH Zurich, Switzerland, in 1990 with a thesis on knowledge-based systems and logic programming. He has been working for Swissair developing expert systems before he joined University of Munich as an Assistant Professor in 1993. Since November 2001, he has been an Associate Professor, now Full Professor at the School of Computing Science of Simon Fraser University, where he co-directs the Database and Data mining research lab. He has published extensively in the top conferences and journals of his field such as ACM SIGKDD, VLDB, ICDM and ICDE, and his work has been very well-cited. His most famous paper on DBSCAN received more than 2900 citations, and his H-number is 34. His current research interests include social network analysis, recommender systems, opinion mining, biological network analysis and high-throughput sequence data analysis. Martin Ester's interests in applications have resulted in various collaborations with research labs, industry and government agencies.

The target audiences of this tutorial will be information retrieval researchers, students who want to make themselves familiar with opinion mining, and practitioners who want to develop state-of-the-art methods for different tasks of opinion mining. Participants will expected to learn the tasks of opinion mining in general as well as methods to mine aspects and ratings from reviews, and will understand the potential and limitations of aspect-based opinion mining and its state-of-the-art methods. The general outline of this tutorial is presented in the following: