Abstract
In recent years, the amount of beauty-related user-created content has steadily increased. Digital beauty-clinic reviews have major impact on user preferences. In supporting user selection decisions, ranking beauty clinics via online reviews is a valuable study subject, although research on this problem is still fairly limited. Sentiment analysis is a very important subject in the research community to evaluate a predefined sentiment from online texts written in a natural language on a particular topic. Recently, research on sentiment analysis for the Arabic language has become popular, since the language has become the fastest-growing language on the web. However, most sentiment-analysis tools are designed for the Modern Standard Arabic language, which is not widely used on social-media platforms. Moreover, the number of lexicons designed to handle the informal Arabic language is restricted, especially in the beauty-clinic-related domain. Besides, numerous sentiment-analysis studies have concentrated on improving the accuracy of sentiment classifiers. Studies about choosing the right company or product on the basis of the results of sentiment analysis are still missing. In decision-analysis domain, the multiattribute-utility theory has been extensively used in selecting the best option among a set of alternatives. Thus, this research aims to propose a systematic methodology that can develop a beauty-clinic-domain-related sentiment lexicon in Saudi dialect, perform sentiment analysis on online reviews of 10 beauty clinics in Riyadh based on the built lexicon, and feed the lexicon-based sentiment analysis results to the multiattribute-utility theory method to evaluate and rank the beauty clinics. Results showed that the Abdelazim Bassam Clinic is Riyadh's best beauty clinic on the basis of the proposed method. The research not only impacts data analysts regarding how to rate beauty clinics on the basis of lexicon-based sentiment-analysis results, but also directs users toward selecting the best beauty clinic.