Demiröz, GülşenYanıkoğlu, BerrinTapucu, DilekSaygın, Yücel03.04. Department of Computer Engineering03. Faculty of Engineering01. Izmir Institute of Technology2017-03-222017-03-222012Demiröz, G., Yanıkoğlu, B., Tapucu, D., and Saygın, Y. (2012, December 10). Learning domain-specific polarity lexicons. Paper presented at the 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012. doi:10.1109/ICDMW.2012.1209780769549255http://doi.org/10.1109/ICDMW.2012.120http://hdl.handle.net/11147/512212th IEEE International Conference on Data Mining Workshops, ICDMW 2012; Brussels; Belgium; 10 December 2012Sentiment 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. © 2012 IEEE.eninfo:eu-repo/semantics/openAccessLexicon adaptationMachine learningNatural language processingPolarity detectionSentiment analysisConference Object2-s2.0-8487315603510.1109/ICDMW.2012.120