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Activity Number: 129
Type: Contributed
Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Marketing
Abstract - #310192
Title: Detecting Consumer Experience Comments off the Web
Author(s): Kurt Pflughoeft*+ and Joseph J. Retzer
Companies: Martiz Research and MarketTools Inc.
Keywords: text mining ; consumer experience ; naive bayesian classifier ; support vector machine
Abstract:

The emergence of consumer-generated media (blogs, forums, social media sites, microblogs, etc..) has demonstrated the need to effectively identify experiential comments. Often times, such sources are littered with irrelevant information such as advertisements and other forms of digital exhaust including those manufactured by reputation management firms. These identification problems are similar to those encountered by email providers that distinguish between spam versus non-spam. Spam detection has often been addressed using a combination of methods including supervised statistical techniques such as Naïve Bayesian Classifiers. Although processing speed is important for email systems, such considerations are less important in identifying experiential comments especially when the content is previously scraped. Consequently, other supervised approaches suited to text processing such as Support Vector Machines will be compared to Naïve Bayesian Classifiers.


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