The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '06 Proceedings of the 23rd international conference on Machine learning
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Movie reviews and revenues: an experiment in text regression
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
Management Science
IEEE Transactions on Knowledge and Data Engineering
Review Graph Based Online Store Review Spammer Detection
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Learning to identify review spam
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Review spam detection via temporal pattern discovery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Word salad: relating food prices and descriptions
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Feature LDA: a supervised topic model for automatic detection of web API documentations from the web
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Mining Permission Request Patterns from Android and Facebook Applications
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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User review is a crucial component of open mobile app markets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose Wiscom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users' reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users' major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.