文章基本信息

标题：Enhancing the Performance of Sentiment Analysis Supervised Learning Using Sentiments Keywords Based Technique
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作者：Amira Abdelwahab ; Fahd Alqasemi ; Hatem Abdelkader 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2017
卷号：7
期号：1
页码：107-116
DOI：10.5121/csit.2017.70111
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Sentiment Analysis (SA) and machine learning techniques are collaborating to understand theattitude of text writer, implied in particular text. Although, SA is an important challengingitself, it is very important challenging in Arabic language. In this paper, we are enhancingsentiment analysis in Arabic language. Our approach had begun with special pre-processingsteps. Then, we had adopted sentiment keywords co-occurrence measure (SKCM), as analgorithm extracted sentiment-based feature selection method. This feature selection methodhad utilized on three sentiment corpora using SVM classifier. We compared our approach withsome traditional methods, followed by most SA works. The experimental results were verypromising for enhancing SA accuracy.
关键词：sentiment analysis; opinion mining; supervised learning; feature selection; Arabic language