Personality exploration system for online social networks
- 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 fromUser-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.…
Author details: | Raad Bin TareafORCiD, Philipp Berger, Patrick Hennig, Christoph MeinelORCiDGND |
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DOI: | https://doi.org/10.1109/WI.2018.00-76 |
ISBN: | 978-1-5386-7325-6 |
Title of parent work (English): | 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) |
Subtitle (English): | Facebook brands as a use case |
Publisher: | IEEE |
Place of publishing: | New York |
Publication type: | Other |
Language: | English |
Date of first publication: | 2019/01/14 |
Publication year: | 2019 |
Release date: | 2022/02/28 |
Tag: | Big Five Model; Brand Personality; Machine Learning; Personality Prediction; Social Media Analysis |
Number of pages: | 9 |
First page: | 301 |
Last Page: | 309 |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |