Title :
Analyzing the Blogosphere for Predicting the Success of Music and Movie Products
Author :
Abel, Fabian ; Diaz-Aviles, Ernesto ; Henze, Nicola ; Krause, Daniel ; Siehndel, Patrick
Author_Institution :
L3S Res. Center, Leibniz Univ. Hannover, Hannover, Germany
Abstract :
Over the last decade blogs became an important part of the Web, where people can announce anything that is on their mind. Due to their high popularity blogs have great potential to mine public opinions regarding products. Such knowledge is very valuable as it could be used to adjust marketing campaigns or advertisement of products accordingly. In this paper we investigate how the blogosphere can be used to predict the success of products in the domain of music and movies. We analyze and characterize the blogging behavior in both domains particularly around product releases, propose different methods for extracting characteristic features from the blogosphere, and show that our predictions correspond to the real world measures Sales Rank and box office revenue respectively.
Keywords :
Web sites; consumer behaviour; data mining; marketing data processing; blogosphere analysis; box office revenue; market campaigning; movie product; music product; product advertisement; public opinion mining; sales ranking; success prediction; Blogs; Correlation; Feature extraction; Machine learning algorithms; Marketing and sales; Motion pictures; Prediction algorithms;
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on
Conference_Location :
Odense
Print_ISBN :
978-1-4244-7787-6
Electronic_ISBN :
978-0-7695-4138-9
DOI :
10.1109/ASONAM.2010.50
