CONSENTO: a new framework for opinion based entity search and summarization

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 "a good birthday present for my girlfriend", "best action movies in 2010", to name a few. Unfortunately, such queries may not 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 segment indexing, as opposed to document indexing, to capture semantics from user opinions more precisely. In particular, we define a new indexing unit, Maximal Coherent Semantic Unit (MCSU).An MCSU represents a segment of a document, which captures a single coherent semantic. We also introduce a new ranking method, called ConsensusRank that counts online comments referring to an entity as a weighted vote. In order to validate the efficacy of the proposed framework, we compare Consento with standard retrieval models and their recent extensions for opinion based entity ranking. Experiments using movie and hotel data show the effectiveness of our framework.