Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
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
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Multilingual Feature-Driven Opinion Extraction and Summarization from Customer Reviews
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Which side are you on?: identifying perspectives at the document and sentence levels
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Sentiment classification using word sub-sequences and dependency sub-trees
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Sentiment strength detection for the social web
Journal of the American Society for Information Science and Technology
That is your evidence?: Classifying stance in online political debate
Decision Support Systems
Stance classification using dialogic properties of persuasion
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Semisupervised learning based opinion summarization and classification for online product reviews
Applied Computational Intelligence and Soft Computing
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In this paper we investigate different approaches we developed in order to classify opinion and discover opinion sources from text, using affect, opinion and attitude lexicon. We apply these approaches on the discussion topics contained in a corpus of American Congressional speech data. We propose three approaches to classifying opinion at the speech segment level, firstly using similarity measures to the affect, opinion and attitude lexicon, secondly dependency analysis and thirdly SVM machine learning. Further, we study the impact of taking into consideration the source of opinion and the consistency in the opinion expressed, and propose three methods to classify opinion at the speaker intervention level, showing improvements over the classification of individual text segments. Finally, we propose a method to identify the party the opinion belongs to, through the identification of specific affective and non-affective lexicon used in the argumentations. We present the results obtained when evaluating the different methods we developed, together with a discussion on the issues encountered and some possible solutions. We conclude that, even at a more general level, our approach performs better than trained classifiers on specific data.