文章基本信息

标题：SENTIMENT ANALYSIS USING HYBRID METHOD OF SUPPORT VECTOR MACHINE AND DECISION TREE
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作者：YASSINE AL-AMRANI ; MOHAMED LAZAAR ; KAMAL EDDINE EL KADIRI 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：7
出版社：Journal of Theoretical and Applied
摘要：The exploitation of social media (forums, blogs and social networks) has become crucial due to the explosive growth of textual data from these new sources of information. Our work focuses on the sentiment analysis resulting from the messages (SMS, Facebook, Twitter...) using original techniques of search of texts. These messages can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. This paper presents a hybrid approach of Support Vector Machine and Decision Tree. This approach permits to ameliorate the result in terms of accuracy and CPU time.
关键词：Sentiment Analysis; Social Media; Classification; Support Vector Machine; Decision Tree