  The impact of online reviews on businesses has grown significantly during
last years, being crucial to determine business success in a wide array of
sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some
users use unethical means to improve their online reputation by writing fake
reviews of their businesses or competitors. Previous research has addressed
fake review detection in a number of domains, such as product or business
reviews in restaurants and hotels. However, in spite of its economical
interest, the domain of consumer electronics businesses has not yet been
thoroughly studied. This article proposes a feature framework for detecting
fake reviews that has been evaluated in the consumer electronics domain. The
contributions are fourfold: (i) Construction of a dataset for classifying fake
reviews in the consumer electronics domain in four different cities based on
scraping techniques; (ii) definition of a feature framework for fake review
detection; (iii) development of a fake review classification method based on
the proposed framework and (iv) evaluation and analysis of the results for each
of the cities under study. We have reached an 82% F-Score on the classification
task and the Ada Boost classifier has been proven to be the best one by
statistical means according to the Friedman test.
