CONSENTO: a consensus search engine for answering subjective queries

Jaehoon Choi, Donghyeon Kim, Seongsoon Kim, Junkyu Lee, Sangrak Lim, Sunwon Lee, Jaewoo Kang

Abstract: Search engines have become an important decision making tool today. Decision making queries are often subjective, such as 'best sedan for family use,' 'best action movies in 2010,' to name a few. Unfortunately, such queries cannot be answered properly by conventional search systems. In order to address this problem, we introduce Consento, a consensus search engine designed to answer subjective queries. Consento performs subdocument-level indexing to more precisely capture semantics from user opinions. We also introduce a new ranking method, or ConsensusRank that counts in online comments referring to an entity as a weighted vote to the entity. We validated the framework with an empirical study using the data on movie reviews.