Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
ACL '05 Proceedings of the 43rd Annual Meeting on Association for 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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Developing Evaluation Model of Topical Term for Document-Level Sentiment Classification
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Adapting svm for data sparseness and imbalance: A case study in information extraction
Natural Language Engineering
Multi-facet Rating of Product Reviews
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Text Categorization Using Fuzzy Proximal SVM and Distributional Clustering of Words
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 05
An iterative reinforcement approach for fine-grained opinion mining
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Towards building a competitive opinion summarization system: challenges and keys
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Supervised latent semantic indexing using adaptive sprinkling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A perspective-based approach for solving textual entailment recognition
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Toward opinion summarization: linking the sources
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Seeing several stars: a rating inference task for a document containing several evaluation criteria
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Text summarisation in progress: a literature review
Artificial Intelligence Review
Can text summaries help predict ratings? a case study of movie reviews
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Sentiment classification of blog posts using topical extracts
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
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We investigate the effect of text summarisation in the problem of rating-inference -- the task of associating a fine-grained numerical rating to an opinionated document. We set-up a comparison framework to study the effect of different summarisation algorithms of various compression rates in this task and compare the classification accuracy of summaries and documents for associating documents to classes. We make use of SVM algorithms to associate numerical ratings to opinionated documents. The algorithms are informed by linguistic and sentiment-based features computed from full documents and summaries. Preliminary results show that some types of summaries could be as effective or better as full documents in this problem.