Academic journal article Journal of Electronic Commerce Research

A Novel Approach to Rate and Summarize Online Reviews According to User-Specified Aspects

Academic journal article Journal of Electronic Commerce Research

A Novel Approach to Rate and Summarize Online Reviews According to User-Specified Aspects

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1. Introduction

The widespread use of network and information technology has led to a wide number of conventional commercial activities being performed online. Many e-commerce systems allow customers to express their opinions regarding the products they have purchased and review the comments posted by previous customers. This option is offered in the hopes of providing reliable, trustworthy information and improving the services they provide. For example, on the hotels.com website, prospective customers can read the reviews written by previous guests about a hotel in which they may be interested. Because these reviews reveal the real experiences of previous customers, they exert a powerful influence on potential customers [Duan et al. 2008; Lu et al. 2014; Zhu et al. 2014; Purnawirawan et al. 2014].

Along with customer reviews, many websites also provide summarized rating information on various predefined aspects of their products and/or services. This helps users to assess review content as quickly as possible [Hu et al. 2012; Gu et al. 2013; Wan & Hakayama 2014]. However, rapid advances in business data analytics have led customers to expect more than just accurate information; they expect better service in the form of information that is both accurate and customized to their needs [Tam & Ho 2006]. Why does customized or personalized information matter? Thirumalai and Sinhab [2011] claim that decision customization that provides choice assistance is positively associated with customer satisfaction. Tam and Ho [2006] also claim that content relevance, self-reference, and goal specificity affect the attention, cognitive processes, and decisions of web users in a variety of ways. In other words, users are receptive to personalized content and find it useful as an aid to decision-making. Although traditional review functions are useful, they fail when the interests of users fall outside the product aspects predefined by the website.

Figure 1 illustrates how online consumer reviews often fail to meet consumer needs. Most existing review websites offer summarized ratings for various aspects of a product, which enables consumers to quickly grasp the content of reviews. In this example, we consider two well-known hotel review websites: hotels.com and booking.com. Hotels.com provides average ratings for each hotel according to the following five predefined aspects: cleanliness, service, comfort, conditions, and neighborhood. Meanwhile, booking.com provides average ratings for each hotel according to the following seven predefined aspects: cleanliness, staff, comfort, facility, location, value for money and free WiFi. It is worth noting that if a customer is interested in the "value for money" or "free WiFi" of a hotel, then hotels.com does not provide the summarized ratings required by the user, because these aspects are not part of their system. A consumer interested in these aspects can then only compare and evaluate the hotels by examining each relevant review one by one, which can be very time-consuming. Or worse yet, a consumer may have an unsatisfactory experience due to the fact that he failed to obtain the required information, leading him to migrate to other websites.

In other words, while information regarding the predefined aspects is helpful in enabling customers to quickly evaluate hotels, it is difficult to acquire an accurate summary from the enormous number of reviews on a website when consumers have unique requirements that are not predefined in the system.

This study therefore proposes a methodology for the rating and clustering of online reviews according to userspecified aspects. For the purposes of testing the proposed methodology, we used hotels.com as a study target; however, our methodology is not specific to this site.

Hotels.com fits the above-mentioned characteristics, in that the system provides an overall rating as well as a summary of average ratings related to cleanliness, service, comfort, conditions, and neighborhoo d. …

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