DocumentCode :
3696921
Title :
Comparison of Four Text Classifiers on Movie Reviews
Author :
Yaguang Wang;Wenlong Fu;Aina Sui;Yuqing Ding
Author_Institution :
Inf. Eng. Sch., Commun. Univ. of China, Beijing, China
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
495
Lastpage :
498
Abstract :
Text Categorization plays an important role in the fields of information retrieval, machine learning, natural language processing, data mining and others. With the development of computer and information technology, there have been many classification algorithms. Each text classification algorithms will get result at differing speeds and efficiency due to the various feature of test text. It has been found that Naive Bayes classifier has a higher accuracy and rate by classifying Movie Reviews in NLTK using Decision Tree classifier, Naive Bayes classifier, Maximum Entropy classifier and K-nearest neighbor classifier.
Keywords :
"Entropy","Classification algorithms","Decision trees","Text categorization","Accuracy","Motion pictures","Training"
Publisher :
ieee
Conference_Titel :
Applied Computing and Information Technology/2nd International Conference on Computational Science and Intelligence (ACIT-CSI), 2015 3rd International Conference on
Type :
conf
DOI :
10.1109/ACIT-CSI.2015.94
Filename :
7336114
Link To Document :
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