Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively, improving the baseline using just the average word polarities by about 2% points.
Feature extraction, Motion pictures, Accuracy, Training, Support vector machines, Conferences, Data mining, machine learning, opinion mining, sentiment analysis, polarity extraction, SentiWordNet, lexicon based methods
Rahim Dehkharghani, Berrin Yanikoglu, Dilek Tapucu, Yucel Saygin, "Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 669-673, doi:10.1109/ICDMW.2012.121