Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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
Extracting knowledge from evaluative text
Proceedings of the 3rd international conference on Knowledge capture
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
A simple but powerful automatic term extraction method
COMPUTERM '02 COLING-02 on COMPUTERM 2002: second international workshop on computational terminology - Volume 14
Text mining for product attribute extraction
ACM SIGKDD Explorations Newsletter
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Proceedings of the ninth international conference on Electronic commerce
Data mining and audience intelligence for advertising
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Entity categorization over large document collections
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Community gravity: measuring bidirectional effects by trust and rating on online social networks
Proceedings of the 18th international conference on World wide web
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Proceedings of the 2010 ACM conference on Computer supported cooperative work
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
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
Consumer adoption of group-buying auctions: an experimental study
Information Technology and Management
Journal of the American Society for Information Science and Technology
Towards a theory model for product search
Proceedings of the 20th international conference on World wide web
Integrating web feed opinions into a corporate data warehouse
Proceedings of the 2nd International Workshop on Business intelligencE and the WEB
Leveraging web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons
Proceedings of the 20th ACM international conference on Information and knowledge management
Manipulation of online reviews: An analysis of ratings, readability, and sentiments
Decision Support Systems
Lexicon-based Comments-oriented News Sentiment Analyzer system
Expert Systems with Applications: An International Journal
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
SumView: A Web-based engine for summarizing product reviews and customer opinions
Expert Systems with Applications: An International Journal
Identifying helpful online reviews: A product designer's perspective
Computer-Aided Design
Implicit feature identification via hybrid association rule mining
Expert Systems with Applications: An International Journal
Are user-contributed reviews community property?: exploring the beliefs and practices of reviewers
Proceedings of the 5th Annual ACM Web Science Conference
Echo: the editor's wisdom with the elegance of a magazine
Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems
Deriving market intelligence from microblogs
Decision Support Systems
A probabilistic mixture model for mining and analyzing product search log
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Storing and analysing voice of the market data in the corporate data warehouse
Information Systems Frontiers
Hi-index | 0.00 |
The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data.