TY  - GEN
A1  - Bin Tareaf, Raad
A1  - Berger, Philipp
A1  - Hennig, Patrick
A1  - Meinel, Christoph
T1  - Personality exploration system for online social networks
BT  - Facebook brands as a use case
T2  - 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)
N2  - User-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
KW  - Big Five Model
KW  - Brand Personality
KW  - Personality Prediction
KW  - Machine Learning
KW  - Social Media Analysis
Y1  - 2019
SN  - 978-1-5386-7325-6
U6  - https://doi.org/10.1109/WI.2018.00-76
SP  - 301
EP  - 309
PB  - IEEE
CY  - New York
ER  -