Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
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.
Motion pictures, Accuracy, Feature extraction, Databases, Computational linguistics, USA Councils, Training, natural language processing, sentiment analysis, lexicon adaptation, polarity detection, machine learning
Gulsen Demiroz, Berrin Yanikoglu, Dilek Tapucu, Yucel Saygin, "Learning Domain-Specific Polarity Lexicons", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 674-679, doi:10.1109/ICDMW.2012.120