Please use this identifier to cite or link to this item: 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
Issue Date: 2012
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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