Customer Opinions Evaluation: A Case Study on Arabic Tweets

Manal Mostafa Ali
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引用次数: 1

Abstract

This paper presents an automatic method for extracting, processing, and analysis of customer opinions on Arabic social media. We present a four-step approach for mining of Arabic tweets. First, Natural Language Processing (NLP) with different types of analyses had performed. Second, we present an automatic and expandable lexicon for Arabic adjectives. The initial lexicon is built using 1350 adjectives as seeds from processing of different datasets in Arabic language. The lexicon is automatically expanded by collecting synonyms and morphemes of each word through Arabic resources and google translate. Third, emotional analysis was considered by two different methods; Machine Learning (ML) and rulebased method. Finally, Feature Selection (FS) is also considered to enhance the mining results. The experimental results reveal that the proposed method outperforms counterpart ones with an improvement margin of up to 4% using F-Measure.
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客户意见评价——以阿拉伯语推文为例
本文提出了一种自动提取、处理和分析阿拉伯社交媒体上客户意见的方法。我们提出了一种挖掘阿拉伯语推文的四步方法。首先,自然语言处理(NLP)进行了不同类型的分析。其次,我们提出了一个自动和可扩展的阿拉伯语形容词词典。最初的词典是用1350个形容词作为种子,从不同的阿拉伯语数据集处理而来。通过阿拉伯语资源和谷歌翻译收集每个单词的同义词和语素,自动扩展词典。第三,情感分析采用了两种不同的方法;机器学习(ML)和基于规则的方法。最后,还考虑了特征选择(FS)来增强挖掘结果。实验结果表明,该方法优于F-Measure的同类方法,改进幅度高达4%。
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