从客户声音到决策者洞察:沙特阿拉伯超级应用程序阿拉伯语评论的文本分析框架

Bodoor Alrayani, Manal Kalkatawi, M. Abulkhair, Felwa A. Abukhodair
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摘要

近来,各商业部门都注重利用超级应用程序等不同的现代技术提供各种服务,以满足客户的需求,创造令人满意的用户体验。因此,研究用户体验已成为研究领域最流行的趋势之一,因为它对企业的繁荣和持续发展起着至关重要的作用。因此,许多研究人员致力于探索和分析社交媒体、博客和网站上的用户体验,并采用机器学习等多种研究方法挖掘用户评论。然而,专注于分析超级应用程序用户体验,特别是挖掘阿拉伯语用户评论的研究还很有限。因此,本文旨在通过使用 biterm 主题建模、CAMeL 情感分析器和 doc2vec 与 k-means 聚类,挖掘沙特阿拉伯阿拉伯语商业领域用户的评论,从而分析和发现超级应用环境中影响用户体验的最重要主题。我们探讨了用户对所提取主题的感受,以确定需要改进的薄弱环节和需要加强的强势环节,从而促进令人满意的用户体验。因此,本文提出了一个阿拉伯语文本注释框架,以帮助沙特阿拉伯的商业部门确定对用户体验有负面和正面影响的重要主题。该框架采用了两种方法:带有情感分析的主题建模和带有聚类的主题建模。结果,建议的框架揭示了四个重要主题:交付和付款、客户服务和更新、价格和应用。对检索到的话题进行了深入研究,结果表明,在大多数话题中,负面评论多于正面评论。这些结果提供了一般性分析和建议,以帮助商业部门提高服务水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From Customer’s Voice to Decision-Maker Insights: Textual Analysis Framework for Arabic Reviews of Saudi Arabia’s Super App
Recently, business sectors have focused on offering a wide variety of services through utilizing different modern technologies such as super apps in order to fulfill customers’ needs and create a satisfactory user experience. Accordingly, studying the user experience has become one of the most popular trends in the research field due to its essential role in business prosperity and continuity. Thus, many researchers have dedicated their efforts to exploring and analyzing the user experience across social media, blogs, and websites, employing a variety of research methods such as machine learning to mine users’ reviews. However, there are limited studies concentrated on analyzing super app users’ experiences and specifically mining Arabic users’ reviews. Therefore, this paper aims to analyze and discover the most important topics that affect the user experience in the super app environment by mining Arabic business sector users’ reviews in Saudi Arabia using biterm topic modeling, CAMeL sentiment analyzer, and doc2vec with k-means clustering. We explore users’ feelings regarding the extracted topics in order to identify the weak aspects to improve and the strong aspects to enhance, which will promote a satisfactory user experience. Hence, this paper proposes an Arabic text annotation framework to help the business sector in Saudi Arabia to determine the important topics with negative and positive impacts on users’ experience. The proposed framework uses two approaches: topic modeling with sentiment analysis and topic modeling with clustering. As a result, the proposed framework reveals four important topics: delivery and payment, customer service and updates, prices, and application. The retrieved topics are thoroughly studied, and the findings show that, in most topics, negative comments outweigh positive comments. These results are provided with general analysis and recommendations to help the business sector to improve its level of services.
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