Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The predictive power of online chatter
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Show me the money!: deriving the pricing power of product features by mining consumer reviews
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
A Unified Framework for Opinion Retrieval
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
HelpMeter: A Nonlinear Model for Predicting the Helpfulness of Online Reviews
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
AMAZING: A sentiment mining and retrieval system
Expert Systems with Applications: An International Journal
'Helpfulness' in online communities: a measure of message quality
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
A quality-aware model for sales prediction using reviews
Proceedings of the 19th international conference on World wide web
S-PLASA+: adaptive sentiment analysis with application to sales performance prediction
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
The long tail or the short tail: The category-specific impact of eWOM on sales distribution
Decision Support Systems
Selecting a comprehensive set of reviews
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-facets quality assessment of online opinionated expressions
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Diversifying Product Review Rankings: Getting the Full Picture
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Text mining and probabilistic language modeling for online review spam detection
ACM Transactions on Management Information Systems (TMIS)
Semi-automatic semantic moderation of web annotations
Proceedings of the 21st international conference companion on World Wide Web
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
ACM Transactions on Management Information Systems (TMIS)
To whom should I listen? Finding reputable reviewers in opinion-sharing communities
Decision Support Systems
Identifying helpful reviews based on customer's mentions about experiences
Expert Systems with Applications: An International Journal
Selecting a characteristic set of reviews
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Review quality aware collaborative filtering
Proceedings of the sixth ACM conference on Recommender systems
Mining millions of reviews: a technique to rank products based on importance of reviews
Proceedings of the 13th International Conference on Electronic Commerce
Stakeholder interaction and internet auction outcomes: analyzing active disclosure
Proceedings of the 13th International Conference on Electronic Commerce
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Analysis of travel review data from reader's point of view
WASSA '12 Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis
Identification of useful user comments in social media: a case study on flickr commons
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Using micro-reviews to select an efficient set of reviews
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers naturally gravitate to reading reviews in order to decide whether to buy a product. However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews. Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews. In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales. Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular. We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums.