Foundations of statistical natural language processing
Foundations of statistical natural language processing
Endogeneity in Brand Choice Models
Management Science
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast Polyhedral Adaptive Conjoint Estimation
Marketing Science
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
Text mining for product attribute extraction
ACM SIGKDD Explorations Newsletter
Observed and Unobserved Preference Heterogeneity in Brand-Choice Models
Marketing Science
Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
Get another label? improving data quality and data mining using multiple, noisy labelers
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
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Knowledge and Data Engineering
Information Systems Research
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
Do Vendors’ Pricing Decisions Fully Reflect Information in Online Reviews?
ACM Transactions on Management Information Systems (TMIS)
Word salad: relating food prices and descriptions
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth
ACM Transactions on Management Information Systems (TMIS)
Journal of Management Information Systems
Why people hate your app: making sense of user feedback in a mobile app store
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
The impact of social and conventional media on firm equity value: A sentiment analysis approach
Decision Support Systems
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Increasingly, user-generated product reviews serve as a valuable source of information for customers making product choices online. The existing literature typically incorporates the impact of product reviews on sales based on numeric variables representing the valence and volume of reviews. In this paper, we posit that the information embedded in product reviews cannot be captured by a single scalar value. Rather, we argue that product reviews are multifaceted, and hence the textual content of product reviews is an important determinant of consumers' choices, over and above the valence and volume of reviews. To demonstrate this, we use text mining to incorporate review text in a consumer choice model by decomposing textual reviews into segments describing different product features. We estimate our model based on a unique data set from Amazon containing sales data and consumer review data for two different groups of products (digital cameras and camcorders) over a 15-month period. We alleviate the problems of data sparsity and of omitted variables by providing two experimental techniques: clustering rare textual opinions based on pointwise mutual information and using externally imposed review semantics. This paper demonstrates how textual data can be used to learn consumers' relative preferences for different product features and also how text can be used for predictive modeling of future changes in sales. This paper was accepted by Ramayya Krishnan, information systems.