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 important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.
The performance of these algorithms did not give the same or even near results when applied to training data labeled with emotions which has the potential of being independent of domain, topic and time REF .
Using Emoticons To Reduce Dependency In Machine Learning Techniques For Sentiment Classification
Annual Meeting Of The Association For Computational Linguistics - Student Research Workshop
Computer Science