Foundations of statistical natural language processing
Foundations of statistical natural language processing
Database merging strategy based on logistic regression
Information Processing and Management: an International Journal
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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
Comparative study of monolingual and multilingual search models for use with asian languages
ACM Transactions on Asian Language Information Processing (TALIP)
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
OpinionFinder: a system for subjectivity analysis
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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In this paper we present a new statistical approach to opinion detection and its' evaluation on the English, Chinese and Japanese corpora. Besides, the proposed method is compared with three baselines, namely Naïve Bayes classifier, a language model and an approach based on significant collocations. These models being language independent are improved with the use of language-dependent technique on the example of the English corpus. We show that our method almost always gives better performance compared to the considered baselines.