文章基本信息

标题：Word Clustering as a Feature for Arabic Sentiment Classification
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作者：Saud Alotaibi ; Charles Anderson
期刊名称：International Journal of Education and Management Engineering(IJEME)
印刷版ISSN：2305-3623
电子版ISSN：2305-8463
出版年度：2017
卷号：7
期号：1
页码：1-13
DOI：10.5815/ijeme.2017.01.01
出版社：MECS Publisher
摘要：Rich morphology language, such as Arabic, requires more investigation and methods targeted toward improving the sentiment analysis task. An example of external knowledge that may provide some semantic relationships within the text is the word clustering technique. This article demonstrates the ongoing work that utilizes word clustering when conducting Arabic sentiment analysis. Our proposed method employs supervised sentiment classification by enriching the feature space model with word cluster information. In addition, the experiments and evaluations that were conducted in this study demonstrated that by combining the clustering feature with sentiment analysis for Arabic, this improved the performance of the classifier.
关键词：Sentiment Classification;Polarity Classification;Arabic Natural Language Processing;Arabic Sentiment Sentence Classification;Machine Learning Classifier;Word Clustering