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https://hdl.handle.net/11147/5122
Title: | Learning domain-specific polarity lexicons | Authors: | Demiröz, Gülşen Yanıkoğlu, Berrin Tapucu, Dilek Saygın, Yücel |
Keywords: | Lexicon adaptation Machine learning Natural language processing Polarity detection Sentiment analysis |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Source: | Demiröz, 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.2012.120 | 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. © 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.2012.120 http://hdl.handle.net/11147/5122 |
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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