Foundations of statistical natural language processing
Foundations of statistical natural language processing
The budgeted maximum coverage problem
Information Processing Letters
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
Optimization, maxent models, and conditional estimation without magic
NAACL-Tutorials '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Tutorials - Volume 5
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Proceedings of the ninth international conference on Electronic commerce
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Mining user reviews: from specification to summarization
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Multi-document summarization via budgeted maximization of submodular functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Efficient confident search in large review corpora
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Summarization of personal photologs using multidimensional content and context
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Selecting a comprehensive set of reviews
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tips, dones and todos: uncovering user profiles in foursquare
Proceedings of the fifth ACM international conference on Web search and data mining
Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions
Proceedings of the 21st international conference on World Wide Web
Selecting a characteristic set of reviews
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
We know where you live: privacy characterization of foursquare behavior
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Online reviews are an invaluable resource for web users trying to make decisions regarding products or services. However, the abundance of review content, as well as the unstructured, lengthy, and verbose nature of reviews make it hard for users to locate the appropriate reviews, and distill the useful information. With the recent growth of social networking and micro-blogging services, we observe the emergence of a new type of online review content, consisting of bite-sized, 140 character-long reviews often posted reactively on the spot via mobile devices. These micro-reviews are short, concise, and focused, nicely complementing the lengthy, elaborate, and verbose nature of full-text reviews. We propose a novel methodology that brings together these two diverse types of review content, to obtain something that is more than the sum of its parts. We use micro-reviews as a crowdsourced way to extract the salient aspects of the reviewed item, and propose a new formulation of the review selection problem that aims to find a small set of reviews that efficiently cover the micro-reviews. Our approach consists of a two-step process: matching review sentences to micro-reviews and then selecting reviews such that we cover as many micro-reviews as possible, with few sentences. We perform a detailed evaluation of all the steps of our methodology using data collected from Foursquare and Yelp.