QER: a new feature selection method for sentiment analysis
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Dosyalar
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Korea Information Processing Soc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Sentiment analysis is about the classification of sentiments expressed in review documents. In order to improve the classification accuracy, feature selection methods are often used to rank features so that non-informative and noisy features with low ranks can be removed. In this study, we propose a new feature selection method, called query expansion ranking, which is based on query expansion term weighting methods from the field of information retrieval. We compare our proposed method with other widely used feature selection methods, including Chi square, information gain, document frequency difference, and optimal orthogonal centroid, using four classifiers: na < ve Bayes multinomial, support vector machines, maximum entropy modelling, and decision trees. We test them on movie and multiple kinds of product reviews for both Turkish and English languages so that we can show their performances for different domains, languages, and classifiers. We observe that our proposed method achieves consistently better performance than other feature selection methods, and query expansion ranking, Chi square, information gain, document frequency difference methods tend to produce better results for both the English and Turkish reviews when tested using na < ve Bayes multinomial classifier.
Açıklama
Anahtar Kelimeler
Sentiment analysis, Feature selection, Machine learning, Text classification
Kaynak
Human-Centric Computing and Information Sciences
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
8
