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
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
An effective statistical approach to blog post opinion retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
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
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A unified relevance model for opinion retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
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
Using Wikipedia and Wiktionary in domain-specific information retrieval
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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
ACM SIGIR Forum
| Hi-index | 0.00 |
Current opinion retrieval techniques do not provide context-dependent relevant results. They use frequency of opinion words in documents or at proximity to query words, such that opinionated documents containing the words are retrieved regardless of their contextual or semantic relevance to the query topic. Thus, opinion retrieved for the qualitative analysis of products, performance measurement for companies, and public reactions to political decisions can be largely biased. We propose a sentence-level linear relevance model that is based on subjective and semantic similarities between predicate-argument structures. This ensures opinionated documents are not only subjective but semantically relevant to the query topic. The linear relevance model performs a linear combination of a popular relevance model, our proposed transformed terms similarity model, and a popular subjectivity mechanism. Evaluation and experimental results show that the use of predicate-argument structures improves performance of opinion retrieval task by more than 15% over popular TREC baselines.