Learning domain-specific polarity lexicons. Demiroz, G., Yan\ikoğlu, B., Tapucu, D., & Sayg\in, Y. In Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, pages 674–679, 2012. IEEE.
Learning domain-specific polarity lexicons [link]Paper  Learning domain-specific polarity lexicons [link]Link  abstract   bibtex   
Sentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. However, since creating domain-specific polarity lexicons is expensive and time consuming, researchers often use a general purpose or domain independent lexicon. In this work, we address the problem of adapting a general purpose polarity lexicon to a specific domain and propose a simple yet effective adaptation algorithm. We experimented with two sets of reviews from the hotel and movie domains and observed that while our adaptation techniques changed the polarity values for only a small set of words, the overall test accuracy increased significantly: 77% to 83% in the hotel dataset and 61% to 66% in the movie dataset.
@inproceedings{demiroz2012learning,
  title={Learning domain-specific polarity lexicons},
  author={Gulsen Demiroz and 
  			Berrin Yan{\i}ko{\u{g}}lu and 
  			Dilek Tapucu and 
  			Y{\"u}cel Sayg{\i}n},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6406504&tag=1},
  booktitle={Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on},
  pages={674--679},
  year={2012},
  ee = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6406504},
  organization={IEEE},
  abstract = {Sentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. However, since creating domain-specific polarity lexicons is expensive and time consuming, researchers often use a general purpose or domain independent lexicon. In this work, we address the problem of adapting a general purpose polarity lexicon to a specific domain and propose a simple yet effective adaptation algorithm. We experimented with two sets of reviews from the hotel and movie domains and observed that while our adaptation techniques changed the polarity values for only a small set of words, the overall test accuracy increased significantly: 77\% to 83\% in the hotel dataset and 61\% to 66\% in the movie dataset.}
}

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