Sentiment analysis of arabic social media content: a comparative study

R. Khasawneh, H. Wahsheh, M. Al-Kabi, I. Alsmadi
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引用次数: 36

Abstract

The Internet became an indispensable part of people's lives because of the significant role it plays in the ways individuals interact, communicate and collaborate with each other. Over recent years, social media sites succeed in attracting a large portion of online users where they become not only content readers but also content generators and publishers. Social media users generate daily a huge volume of comments and reviews related to different aspects of life including: political, scientific and social subjects. In general, sentiment analysis refers to the task of identifying positive and negative opinions, emotions and evaluations related to an article, news, products, services, etc. Arabic sentiment analysis is conducted in this study using a small dataset consisting of 1,000 Arabic reviews and comments collected from Facebook and Twitter social network websites. The collected dataset is used in order to conduct a comparison between two free online sentiment analysis tools: SocialMention and SentiStrength that support Arabic language. The results which based on based on the two of classifiers (Decision tree (J48) and SVM) showed that the SentiStrength is better than SocialMention tool.
阿拉伯语社交媒体内容的情感分析:比较研究
互联网成为人们生活中不可或缺的一部分,因为它在个人互动、沟通和协作方面发挥着重要作用。近年来,社交媒体网站成功地吸引了大量的在线用户,他们不仅成为内容的读者,而且成为内容的创造者和发布者。社交媒体用户每天都会产生大量与生活不同方面有关的评论和评论,包括:政治、科学和社会主题。一般来说,情感分析是指识别与文章、新闻、产品、服务等相关的积极和消极的意见、情绪和评价的任务。本研究使用一个小数据集进行阿拉伯情绪分析,该数据集包括从Facebook和Twitter社交网站收集的1000条阿拉伯评论和评论。收集的数据集用于在两个免费的在线情感分析工具之间进行比较:SocialMention和SentiStrength,这两个工具支持阿拉伯语。基于决策树(J48)和支持向量机(SVM)两种分类器的结果表明,SentiStrength优于SocialMention工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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