The user reviews of mobile apps are important assets that reflect the users' needs and complaints about particular apps regarding features, usability, and designs. From investigating the content of such reviews, the app developers can acquire useful information guiding the future maintenance and evolution work. Previous studies on opinion mining in mobile app reviews have provided various approaches to eliciting such critical information. A particular update of an app can provide changes to the app that result in users' reversed opinions, as well as, specific new complaints or praises. However, limited studies focus on eliciting the user opinions regarding a particular mobile app update, or the impact the update imposes. In this paper, we propose a method for systematically studying and analyzing the evolution of the users' opinions taking into consideration a set of mobile app updates. For doing so, we compare the topics appearing in the users' reviews before and after the updates. We also validate the method with an experiment on an existing mobile app.