Effectiveness of SVM Method by Naïve Bayes Weighting in Movie Review Classification

Fadli Fauzi Zain(1*), Yuliant Sibaroni(2),

(1) Telkom University
(2) Telkom University
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v5i2.7770

Abstract

Classification of movie review belongs to the realm of text classification, especially in the field of sentiment analysis. One familiar text classification method used is support vector maching (SVM) and Naïve Bayes. Both of these methods are known to have good performance in handling text classification separately. Combining these two methods is expected to improve the performance of classifier compared to working separately. This paper reports the effort to classify movie reviews using the combined method of Naïve Bayes and SVM with Naïve Bayes as weights. This combined method is commonly called NBSVM. The results showed the best accuracy is obtained if the classification is done by the NBSVM method, which is equal to 88.8% with the combined features of unigram and bigram and using pre-processing in the form of data cleansing only.

Keywords

movie review; NBSVM; NaiveBayes; SVM; text mining

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