Automatic Categorization of Reviews and Opinions of Internet: E-Shopping Customers

Data Description

To investigate possibilities of automatic data-mining from customer comments that are written in a quite free, unstructured form using natural language (Berry & Kogan, 2010; Konchady, 2006), the authors collected some publicly accessible textual data from the Internet web-site amazon.com. The main intention was to get comments about various consumer goods with at least 100 different opinions per each goods item provided by purchasers. The customer reviews describe their experiences that are good, bad, or something between. It is possible to apply also a certain scale as a kind of classification, or rating: from one star (the worst experience) up to five stars (the best one). The reviews are expected to explain reasons of their ratings which are usually relatively short, tens or hundreds of words. Typically, the language is English, however, with many mistypings, grammar errors, and so forth. In addition, the used English is really very “international”, and the customers are not only people whose native language is one of existing English languages that can more or less differ in grammar and vocabulary. Also, a reader of reviews can sometimes see non-standard interjections and onomatopoeic words.

The nine different commodities the reviews of which were used in the research are shown in Table 1. Interestingly, the average customer rating is very typically closer to five stars which means that customers were probably mostly satisfied.