Paper: Using Emoticons To Reduce Dependency In Machine Learning Techniques For Sentiment Classification

ACL ID P05-2008
Title Using Emoticons To Reduce Dependency In Machine Learning Techniques For Sentiment Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

Sentiment Classification seeks to identify a piece of text according to its author’s general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. This paper demonstrates that match with respect to domain and time is also impor- tant, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being indepen- dent of domain, topic and time.