Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches

Islam Obaidat, Rami Mohawesh, M. Al-Ayyoub, Mohammad Al-Smadi, Y. Jararweh
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引用次数: 53

Abstract

Sentiment Analysis (SA) is the process of determining the sentiment of a text written in a natural language to be positive, negative or neutral. It is one of the most interesting subfields of natural language processing (NLP) and Web mining due to its diverse applications and the challenges associated with applying it on the massive amounts of textual data available online (especially, on social networks). Most of the current works on SA focus on the English language and work on the sentence-level or the document-level. This work focuses on the less studied version of SA, which is aspect-based SA (ABSA) for the Arabic language. Specifically, this work considers two ABSA tasks: aspect category determination and aspect category polarity determination, and makes use of the publicly available human annotated Arabic dataset (HAAD) along with its baseline experiments conducted by HAAD providers. In this work, several lexicon-based approaches are presented for the two tasks at hand and show that some of the presented approaches significantly outperforms the best known result on the given dataset.
用基于词典的方法增强阿拉伯文评论中方面类别及其极性的确定
情感分析(SA)是确定用自然语言写成的文本的情感是积极的、消极的或中立的过程。它是自然语言处理(NLP)和Web挖掘中最有趣的子领域之一,因为它有各种各样的应用程序,以及将其应用于在线(特别是社交网络)上的大量文本数据所带来的挑战。目前关于情景识别的研究大多集中在英语语言层面,主要集中在句子层面或文档层面。这项工作的重点是研究较少的SA版本,即阿拉伯语的基于方面的SA (ABSA)。具体来说,这项工作考虑了两个ABSA任务:方面类别确定和方面类别极性确定,并利用了公开可用的人类注释阿拉伯语数据集(HAAD)及其由HAAD提供者进行的基线实验。在这项工作中,针对手头的两个任务提出了几种基于词典的方法,并表明其中一些方法在给定数据集上的性能明显优于已知的最佳结果。
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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