期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：3
DOI：10.14569/IJACSA.2018.090307
出版社：Science and Information Society (SAI)
摘要：Sentiment analysis is utilised to assess users’ feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users’ feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): “earnings” (ارباح), “losses” (خسائر), “green colour” (باللون_الاخضر), “growing” (زياده), “distribution” (توزيع), “decrease” (انخفاض), “financial penalty” (غرامة), and “delay” (تاجيل). Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%.
关键词：Opinion mining; association rule; Arabic language; sentiment analysis; Twitter
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