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This paper presents a corpus study of evaluative and speculative language. Knowledge of such language would be useful in many applications, such as text categorization and summarization. Analyses of annotator agreement and of characteristics of subjective language are performed. This study yields knowledge needed to design effective machine learning systems for identifying subjective language.
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Wiebe et al. (2001) studied this question.