Foundations of statistical natural language processing
Foundations of statistical natural language processing
Endogeneity in Brand Choice Models
Management Science
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Marketing Models of Service and Relationships
Marketing Science
Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
Accounting for Primary and Secondary Demand Effects with Aggregate Data
Marketing Science
Red Opal: product-feature scoring from reviews
Proceedings of the 8th ACM conference on Electronic commerce
Do online reviews matter? - An empirical investigation of panel data
Decision Support Systems
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Research Commentary---Sponsored Search and Market Efficiency
Information Systems Research
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
Management Science
IEEE Transactions on Knowledge and Data Engineering
MIS Quarterly
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User-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers' multidimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique data set of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. By doing so, we can provide customers with the “best-value” hotels early on. Our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. On a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. We thus highlight the tight linkages between user behavior on social media and search engines. Our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research.