Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Aspect-based sentence segmentation for sentiment summarization
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
ACM SIGIR Forum
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
Going beyond traditional QA systems: challenges and keys in opinion question answering
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Opinion word expansion and target extraction through double propagation
Computational Linguistics
A fast, accurate, non-projective, semantically-enriched parser
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
CONSENTO: a consensus search engine for answering subjective queries
Proceedings of the 21st international conference companion on World Wide Web
Information Retrieval
Generating comparative summaries from reviews
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.