Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5128
Title: Adaptation and use of subjectivity lexicons for domain dependent sentiment classification
Authors: Dehkharghani, Rahim
Yanıkoğlu, Berrin
Tapucu, Dilek
Saygın, Yücel
Tapucu, Dilek
Izmir Institute of Technology. Computer Engineering
Keywords: Lexicon based methods
Machine learning
Opinion mining
Polarity extraction
Sentiment analysis
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Dehkharghani, R., Yanıkoğlu, B., Tapucu, D., and Saygın, Y. (2012, December 10). Adaptation and use of subjectivity lexicons for domain dependent sentiment classification. Paper presented at the 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012. doi:10.1109/ICDMW.2012.121
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. © 2012 IEEE.
Description: 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012; Brussels; Belgium; 10 December 2012
URI: http://doi.org/10.1109/ICDMW.2012.121
http://hdl.handle.net/11147/5128
ISBN: 9780769549255
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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