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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.

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