Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
More than words: syntactic packaging and implicit sentiment
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
The design, implementation, and use of the Ngram statistics package
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Creating speech and language data with Amazon's Mechanical Turk
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
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This paper considers the linguistic indicators of bias in political text. We used Amazon Mechanical Turk judgments about sentences from American political blogs, asking annotators to indicate whether a sentence showed bias, and if so, in which political direction and through which word tokens. We also asked annotators questions about their own political views. We conducted a preliminary analysis of the data, exploring how different groups perceive bias in different blogs, and showing some lexical indicators strongly associated with perceived bias.