TY - GEN
T1 - CONSENTO
T2 - 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
AU - Choi, Jaehoon
AU - Kim, Donghyeon
AU - Kim, Seongsoon
AU - Lee, Junkyu
AU - Lim, Sangrak
AU - Lee, Sunwon
AU - Kang, Jaewoo
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - consensus rank
KW - consensus search
KW - entity search
KW - maximal coherent semantic unit
KW - sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=84871052205&partnerID=8YFLogxK
U2 - 10.1145/2396761.2398547
DO - 10.1145/2396761.2398547
M3 - Conference contribution
AN - SCOPUS:84871052205
SN - 9781450311564
T3 - ACM International Conference Proceeding Series
SP - 1935
EP - 1939
BT - CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Y2 - 29 October 2012 through 2 November 2012
ER -