Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Conversational Case-Based Reasoning
Applied Intelligence
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Learning to recommend helpful hotel reviews
Proceedings of the third ACM conference on Recommender systems
Merging multiple criteria to identify suspicious reviews
Proceedings of the fourth ACM conference on Recommender systems
Distortion as a validation criterion in the identification of suspicious reviews
Proceedings of the First Workshop on Social Media Analytics
RELFIN – topic discovery for ontology enhancement and annotation
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
The demonstration of the reviewer's assistant
Proceedings of the sixth ACM conference on Recommender systems
Leveraging the crowd to improve feature-sentiment analysis of user reviews
Proceedings of the 2013 international conference on Intelligent user interfaces
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User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. Indeed this has become a vital part of the service provided by sites like Amazon and TripAdvisor, so much so that many of us will routinely check appropriate product reviews before making a purchase decision, regardless of whether we intend to purchase online or not. The importance of reviews has highlighted the need to help users to produce better reviews and in this paper we describe the development and evaluation of a Reviewer's Assistant for this purpose. We describe a browser plugin that is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. These suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review. We describe and evaluate a number of different algorithms to identify useful topics to recommend to the user and go on to describe the results of a preliminary live-user trial.