Title :
Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification
Author :
Dehkharghani, Rahim ; Yanikoglu, Benin ; Tapucu, D. ; Saygin, Yucel
Author_Institution :
Dept. of Comput. Sci. & Eng., Sabanci Univ., Istanbul, Turkey
Abstract :
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.
Keywords :
computational linguistics; data mining; feature extraction; natural language processing; text analysis; automatic sentiment extraction; average word polarity; domain dependent sentiment classification; natural language text; sentiment analysis; subjectivity lexicon; subjectivity-based feature extraction; Accuracy; Conferences; Data mining; Feature extraction; Motion pictures; Support vector machines; Training; SentiWordNet; lexicon based methods; machine learning; opinion mining; polarity extraction; sentiment analysis;
Conference_Titel :
Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4673-5164-5
DOI :
10.1109/ICDMW.2012.121
