Users review about an app is a crucial component for open mobile application market, such as the AppStore and the Google play. Analyzing these reviews can reveal user's sentiment towards a feature in the app. There exist several analytical tools to summarize user reviews and extract meaningful sense out of them. However, these tools are still limited in terms of expressiveness and accurately classifying the reviews into more than a positive and a negative review. There is a need to get more insights from user app reviews and direct it to future app development. In this paper, we present our result of analyzing user reviews of 20 food journaling and health tracking apps. We gathered and analyzed reviews per app and classified them into three distinct categories using the sentiment treebank with recursive neural tensor network. We then analyzed the vocabulary frequency per category using the Gensim implementation of Word2Vec model. The analysis result clustered the reviews into good, bad and ugly feature reviews. Different usage patterns were detected from users review. We identified major reasons why users express a certain sentiment towards an app and learned how users' satisfaction or complaints was related to a specific feature. This research could be a guideline for app developers to follow when developing an app to refrain from adopting techniques that might demotivate (hinder) the application use or adopt those perceived positively by the users.