{'fraction_non_alphanumeric': 0.014691478942213516, 'fraction_numerical': 0.0039177277179236044, 'mean_word_length': 4.7740112994350286, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mining User Reviews for Mobile App Comparisons

As the number of mobile apps keeps increasing, users often need to compare many apps, in order to choose one that best fits their needs. Fortunately, as there are so many users sharing an app market, it is likely that some other users with the same preferences have already made the comparisons and shared their opinions. For example, a user may state that an app is better in power consumption than another app in a review, then the review would help other users who care about battery life while choosing apps. This paper presents a method to identify comparative reviews for mobile apps from an app market, which can be used to provide fine-grained app comparisons based on different topics. According to experiments on 5 million reviews from Google Play and manual assessments on 900 reviews, our method is able to identify opinions accurately and provide meaningful comparisons between apps, which could in turn help users find desired apps based on their preferences.