Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Facts or friends?: distinguishing informational and conversational questions in social Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Learning to recommend helpful hotel reviews
Proceedings of the third ACM conference on Recommender systems
An assessment of machine learning techniques for review recommendation
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Credibility-inspired ranking for blog post retrieval
Information Retrieval
Fake reviews: the malicious perspective
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Have you done anything like that?: predicting performance using inter-category reputation
Proceedings of the sixth ACM international conference on Web search and data mining
Topic extraction from online reviews for classification and recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
User-generated content provides online consumers with a wealth of information. Given the ever-increasing quantity of available content and the lack of quality control applied to this content, there is a clear need to enhance the user experience when it comes to effectively leveraging this vast information source. In this paper, we address these issues in the context of user-generated product reviews. We expand on recent work to consider the performance of structural and readability feature sets on the classification of helpful product reviews. Our findings, based on a large-scale evaluation of TripAdvisor and Amazon reviews, indicate that structural and readability features are useful predictors for Amazon product reviews but less so for TripAdvisor hotel reviews.