Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The syntactic process
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Identifying semantic roles using Combinatory Categorial Grammar
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
A comparative study of methods for estimating query language models with pseudo feedback
Proceedings of the 18th ACM conference on Information and knowledge management
Automatic creation of a reference corpus for political opinion mining in user-generated content
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends in Information Retrieval
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
Optimizing two stage bigram language models for IR
Proceedings of the 19th international conference on World wide web
Proximity-based opinion retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
High precision opinion retrieval using sentiment-relevance flows
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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We present the results of our experiment on the use of predicate-argument structures containing subjective adjectives for semantic-based opinion retrieval. The approach exploits the grammatical tree derivation of sentences to show the underlying meanings through the respective predicate-argument structures. The underlying meaning of each subjective sentence is then semantically compared with the underlying meaning of the query topic given in natural language sentence. Rather than using frequency of opinion words or their proximity to query words, our solution is based on frequency of semantically related subjective sentences. We formed a linear relevance model that uses explicit and implicit semantic similarities between predicate-argument structures of subjective sentences and the given query topic. Thus, the technique ensures that opinionated documents retrieved are not only subjective but have semantic relevance to the given query topic. Experimental results show that the technique improves performance of topical opinion retrieval task.