Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Limits of opinion-finding baseline systems
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
Integrating Proximity to Subjective Sentences for Blog Opinion Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Semantic role labeling via tree kernel joint inference
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Joint extraction of entities and relations for opinion recognition
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
Proximity-based opinion retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Generating focused topic-specific sentiment lexicons
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Reranking models in fine-grained opinion analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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Opinion retrieval is the task of finding documents that express an opinion about a given query. A key challenge in opinion retrieval is to capture the query-related opinion score of a document. Existing methods rely mainly on the proximity information between the opinion terms and the query terms to address the key challenge. In this study, we propose to incorporate the syntactic and semantic information of terms into a probabilistic model to capture the query-related opinion score more accurately. The syntactic tree structure of a sentence is used to evaluate the modifying probability between an opinion term and a noun within the sentence with a tree kernel method. Moreover, WordNet and the probabilistic topic model are used to evaluate the semantic relatedness between any noun and the given query. The experimental results over standard TREC baselines on the benchmark BLOG06 collection demonstrate the effectiveness of our proposed method, in comparison with the proximity-based method and other baselines. © 2012 Wiley Periodicals, Inc.