عنوان	Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification
نوع پژوهش	مقاله ارائه شده
کلیدواژه‌ها	opinion mining; sentiment analysis; polarity extraction; SentiWordNet; lexicon based methods; machine learning;
چکیده	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.
پژوهشگران	رحیم دهخوارقانی (نفر اول)، Yucel Saygin (نفر چهارم)، Berrin Yanikoglu (نفر دوم)، Dilek Tapucu (نفر سوم)