Making large-scale support vector machine learning practical
Advances in kernel methods
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Statistical QA - classifier vs. re-ranker: what's the difference?
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
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
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
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 blog polarity classification via topic analysis and adaptive methods
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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Though polarity classification has been extensively explored at various text levels and domains, there has been only comparatively little work looking into topic-related polarity classification. This paper takes a detailed look at how sentences expressing a polar attitude towards a given topic can be retrieved from a blog collection. A cascade of independent text classifiers on top of a sentence-retrieval engine is a solution with limited effectiveness. We show that more sophisticated processing is necessary. In this context, we not only investigate the impact of a more precise detection and disambiguation of polar expressions beyond simple text classification but also inspect the usefulness of a joint analysis of topic terms and polar expressions. In particular, we examine whether any syntactic information is beneficial in this classification task.