MINING CONSUMER OPINIONS ON THE WEB - Organizational Learning from Online Consumer-to-Consumer Interactions

Irene Pollach



Consumer-opinion websites are becoming important sources of marketing intelligence for companies, enabling them to turn consumer opinions into opportunities for enhancing customer satisfaction. Grounded in media richness theory, this paper examines a sample of consumer-opinion websites to identify mechanisms that render the information disseminated on these websites more suitable for data mining activities. The results indicate that feedback mechanisms, member profiles, active hyperlinks, and spellcheckers are means of raising data quality. However, a key challenge for mining consumer opinions remains the identification and elimination of emotional content such as humor.


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Paper Citation

in Harvard Style

Pollach I. (2007). MINING CONSUMER OPINIONS ON THE WEB - Organizational Learning from Online Consumer-to-Consumer Interactions . In Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST, ISBN 978-972-8865-79-5, pages 129-134. DOI: 10.5220/0001275101290134

in Bibtex Style

author={Irene Pollach},
title={MINING CONSUMER OPINIONS ON THE WEB - Organizational Learning from Online Consumer-to-Consumer Interactions},
booktitle={Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST,},

in EndNote Style

JO - Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST,
TI - MINING CONSUMER OPINIONS ON THE WEB - Organizational Learning from Online Consumer-to-Consumer Interactions
SN - 978-972-8865-79-5
AU - Pollach I.
PY - 2007
SP - 129
EP - 134
DO - 10.5220/0001275101290134