文章基本信息

标题：AUTOMATIC SEMANTIC SENTIMENT ANALYSIS ON TWITTER TWEETS USING MACHINE LEARNING: A COMPARATIVE STUDY
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作者：SARAH M. ALSUBAIE ; KHOLOUD M. ALMUTAIRI ; NAJLA A. ALNUAIM 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：2019
卷号：97
期号：23
页码：3497-3508
出版社：Journal of Theoretical and Applied
摘要：Due to multiple reasons, social media and microblogs have gained a lot of interest from researchers in the field of Sentiment Analysis recently. Social media platforms comprise one of the most perfect environments of speech and mind expression. This study aims to perform Sentiment Analysis on Twitter platform to identify the polarity of tweets involved in a trending hashtag or event in Twitter. The chosen method for this study is to use ensemble Machine Learning approach using Na�ve Bayesian combined with Support Vector Machine, followed by semantic analysis to improve its accuracy. The outcome of the proposed model will be able to determine the polarity of any given text "tweet" to generate a comprehensive statistical report regarding the public's opinion in a certain matter. These reports can be beneficial to marketing specialists, managers, and even Governments to collect the population thinking in order to enhance the standards of living in a region.
关键词：Sentiment Analysis (SA); Twitter; Multinomial Na�ve Bayes (Multinomial NB); Support Vector Machine (SVM); Classifier Ensemble (CE); Machine Learning (ML)