SAMAR: Subjectivity and sentiment analysis for Arabic social media
SAMAR: Subjectivity and sentiment analysis for Arabic social media
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DOI：
10.1016/j.csl.2013.03.001
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发表时间：
2014-01-01
影响因子：
4.3
通讯作者：
Kuebler, Sandra
中科院分区：
文献类型：
--
作者：
Abdul-Mageed, Muhammad;Diab, Mona;Kuebler, Sandra
SAMAR是一个针对阿拉伯社交媒体类型的主观性和情感分析（SSA）系统。阿拉伯语是一种形态丰富的语言，这为构建专为英语设计的SSA系统的标准方法带来了显着的复杂性。除了社交媒体类型处理带来的困难之外，阿拉伯语本身具有大量的可变单词形式，导致数据稀疏。在这种情况下，我们解决以下4个相关问题：如何最好地表示词汇信息;是否用于英语的标准功能是有用的阿拉伯语;如何处理阿拉伯方言;以及，是否体裁的具体功能有可衡量的影响性能。我们的研究结果表明，使用词元或词素信息是有帮助的，以及使用两个部分的语音标记集（RTS和Erts）。然而，结果表明，我们需要个性化的解决方案，为每一种体裁和任务，但词形还原和Erts词性标记集是目前在大多数的设置。(C)2013爱思唯尔有限公司保留所有权利。
SAMAR is a system for subjectivity and sentiment analysis (SSA) for Arabic social media genres. Arabic is a morphologically rich language, which presents significant complexities for standard approaches to building SSA systems designed for the English language. Apart from the difficulties presented by the social media genres processing, the Arabic language inherently has a high number of variable word forms leading to data sparsity. In this context, we address the following 4 pertinent issues: how to best represent lexical information; whether standard features used for English are useful for Arabic; how to handle Arabic dialects; and, whether genre specific features have a measurable impact on performance. Our results show that using either lemma or lexeme information is helpful, as well as using the two part of speech tagsets (RTS and ERTS). However, the results show that we need individualized solutions for each genre and task, but that lemmatization and the ERTS POS tagset are present in a majority of the settings. (C) 2013 Elsevier Ltd. All rights reserved.