Adaptation and use of subjectivity lexicons for domain dependent sentiment classification. Dehkharghani, R., Yan\ikoğlu, B., Tapucu, D., & Sayg\in, Y. In Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, pages 669–673, 2012. IEEE.
Adaptation and use of subjectivity lexicons for domain dependent sentiment classification [link]Paper  Adaptation and use of subjectivity lexicons for domain dependent sentiment classification [link]Link  abstract   bibtex   
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.
@inproceedings{dehkharghani2012adaptation,
  title={Adaptation and use of subjectivity lexicons for domain dependent sentiment classification},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6406503},
  author={Rahim Dehkharghani and
			Berrin Yan{\i}ko{\u{g}}lu and 
  			Dilek Tapucu and 
  			Y{\"u}cel Sayg{\i}n},
  booktitle={Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on},
  pages={669--673},
  year={2012},
  ee = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6406503},
  organization={IEEE},
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.}
}

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