# Effects of the Hybrid CRITIC-VIKOR Method on Product Aspect Ranking in Customer Reviews
## 1 Introduction  
Customers can freely share their thoughts and experiences regarding goods and services anytime and anyplace thanks to the social web. By giving customers knowledge about goods and services that other people have used, online reviews, like other electronic word of mouth (eWOM) methods, help to reduce the uncertainty associated with online shopping [1]. Therefore, the use of surveys to collect the opinions of customers about products and subsequently measure the degree of customer satisfaction may no longer be needed for organizations, especially since such information is already available online given the explosive growth of social networks [2, 3]. Moreover, online user feedback can be accessed in real time and does not involve any costs. Without the need for expensive survey initiatives, firms can now access a multitude of easily available consumer reviews to learn more about how well their products function, pinpoint areas for improvement, and make wise business decisions. Utilizing this beneficial resource enables businesses to communicate with their clients directly and anticipate their wants and preferences. Additionally,the openness of consumer reviews on e-commerce websites encourages accountability and transparency, raising the platform's general credibility and dependability. Nonetheless, the massive growth of online opinion data has overwhelmed users and firms such that manually tracking these online reviews has become a dreadful task [1]. For instance, for TripAdvisor, which is considered one of the best review platforms to find information regarding accommodation, hotels, and other services, the number of online reviews on this platform has grown from 200 million in 2014 to more than 600 million in 2017 [4]. The valuable knowledge extractable from high-volume online reviews that can benefit customers and firms has inspired researchers to design automatic review approaches. Most of their studies have focused on techniques to identify product features and the sentiments of users regarding each aspect based on NLP and statistical techniques; however, the ranking and prioritization of critical aspects that can considerably affect the decisions of customers and firms are rarely investigated [5; 6; 7]. Some of the studies have performed a ranking process for candidate product aspects based on the statistical information of their occurrences as a complementary task to the extraction process. Nonetheless, the significance of these aspects differs in their influence on the satisfaction of customers toward a product. For instance, some aspects of the _iPhone_, such as _\"battery\"_ and _\"usability\"_, are considered more important than _\"usb\"_ and _\"button\"_. Furthermore, customers search for quality information in website reviews. Guiding customers to focus on important product aspects will allow them to make wise purchasing decisions. In order to help them make informed purchasing decisions, buyers actively seek out reliable information in online evaluations. They wish to concentrate on the factors that are crucial and significantly affect their enjoyment. Therefore, it becomes crucial to have a solid and trustworthy process for methodically rating and prioritizing important factors. Customers can make informed selections and choose products that match their unique needs and tastes by receiving clear instructions on the significance of various product characteristics. Additionally, organizations are better equipped to prioritize product developments and spend resources efficiently when they comprehend the relative importance of various components. Businesses can strategically improve their products and obtain an advantage in the market by concentrating on the factors that have the biggest effects on consumer happiness. A novel framework was proposed [8] that combined sentiment analysis and multi-criteria decision making (MCDM) to rank multiple alternatives based on three extraction criteria: frequency-based product aspects, opinion-related product aspects, and domain-related aspects.