文章基本信息

标题：Aspect-based sentiment analysis using smart government review data
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作者：Omar Alqaryouti ; Nur Siyam ; Azza Abdel Monem 等
期刊名称：Applied Computing and Informatics
印刷版ISSN：2210-8327
电子版ISSN：2210-8327
出版年度：2019
页码：1-10
DOI：10.1016/j.aci.2019.11.003
出版社：Elsevier
摘要：Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.
关键词：Sentiment analysis ; Aspect extraction ; Aspect;based sentiment analysis ; Lexicon approach ; Rule;based approach ; Government smart apps