Machine translation (MT) has surpassed all quality expectations and its use has increased exponentially in recent years (Forcada 2017; S & aacute;nchez-Gij & oacute;n, Moorkens, and Way 2019). One of the most frequent MT applications is the translation of user-generated content (UGC) and, more specifically, reviews on tourism portals such as Tripadvisor. Several authors agree that the degree of trust and credibility of a review, as the most important characteristics of UGC, depends largely on the perceived naturalness and authenticity of its writing (Pollach 2006; Schemmann 2011; V & aacute;squez 2014). The review's influence on the product's reputation and on the purchase decision-making of future users has been fully demonstrated. Since review platforms make reviews available to users in different languages translated by MT, the quality of MT output should be studied from the point of view of the text's adaptation to the requirements of a specific audience and market, following the principles elaborated in localization studies. The aim of this paper is to analyze the behavior of neural MT of user-generated content from the perspective of localization to check whether MT quality depends exclusively on linguistic or stylistic aspects or whether the aspects studied by localization, such as linguistic and cultural appropriateness for the target user, also play a decisive role. We compiled an English-Spanish parallel corpus consisting of 250 reviews retrieved from Tripadvisor. The reviews were originally written in English and MT processed into Spanish. Then the quality of the MT output was evaluated following two parameters: correctness and acceptance according to MT quality scales and localization guidelines.