PhD Dissertation Proposal by Maksim TKACHENKO | Comparison Mining from Text

Comparison Mining from Text

About The Talk

Online opinions are important in many spheres of our lives, and their systematic analysis is a real-life problem. Due to enormous amount of opinions scattered across the Web, handcrafted analysis seems to carry inadmissible cost of time and efforts. An alternative to consider is an automated or, more appropriately, semi-automated analysis conducted by computers as an assistance to a human analyst.

Comparison mining aims at understanding the opinion mining problem, when multiple entities are present simultaneously. This includes, but is not limited to deriving similarities and differences between entities and discovering information about the entity relations. The notion of comparison tangle in in a form of joint evaluative statements, such as “I think A is better than B”, “I think A is a good alternative to B”, and introduces new research questions, similar and yet different from traditional opinion mining. How do we find these statements in reviews? How do we interpret these statements? How do we make sense of thousands of such comparisons? In this talk, we seek to answer these questions and propose a set of computational methods, which deal with the related tasks.

Speaker Biography

Maksim TKACHENKO is a PhD candidate at Singapore Management University (SMU). He received his diploma in mathematics and software engineering from Saint Petersburg State University, Russia, where afterwards he served as a research engineer. At SMU, his research focuses on text mining and natural language processing methods for user preference acquisition.