Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning Subjective Adjectives from Corpora
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Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining newsgroups using networks arising from social behavior
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)
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Learning subjective nouns using extraction pattern bootstrapping
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
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
Identifying and classifying subjective claims
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
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
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A new framework for analyzing political news
Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government
Finding Appropriate Turning Point for Text Sentiment Polarity
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
An analysis of perspectives in interactive settings
Proceedings of the First Workshop on Social Media Analytics
Don't turn social media into another 'Literary Digest' poll
Communications of the ACM
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape
Social Science Computer Review
Mining web query logs to analyze political issues
Proceedings of the 3rd Annual ACM Web Science Conference
Journal of the American Society for Information Science and Technology
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Recently there has been increasing interest in constructing general-purpose political opinion classifiers for applications in e-Rulemaking. This problem is generally modeled as a sentiment classification task in a new domain. However, the classification accuracy is not as good as that in other domains such as customer reviews. In this paper, we report the results of a series of experiments designed to explore the characteristics of political opinion expression which might affect the sentiment classification performance. We found that the average sentiment level of Congressional debate is higher than that of neutral news articles, but lower than that of movie reviews. Also unlike the adjective-centered sentiment expression in movie reviews, the choice of topics, as reflected in nouns, serves as an important mode of political opinion expression. Manual annotation results demonstrate that a significant number of political opinions are expressed in neutral tones. These characteristics suggest that recognizing the sentiment is not enough for political opinion classification. Instead, what seems to be needed is a more fine-grained model of individuals' ideological positions and the different ways in which those positions manifest themselves in political discourse.