TY - GEN
T1 - Automated discovery of product preferences in ubiquitous social media data
T2 - 20th International Computer Science and Engineering Conference, ICSEC 2016
AU - Tuarob, Suppawong
AU - Tucker, Conrad S.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/21
Y1 - 2017/2/21
N2 - Social media enables ubiquitous communication that allows users to disseminate and receive information anywhere and anytime. Among this increasingly vast pool of social media data reside opinionate messages that infer user experience on product usages. Knowledge extracted from such messages could prove to be useful to manufacturers and designers looking to develop next generation products that better meet the needs of the market. Recent developments in machine learning algorithms make it possible to analyze and automatically discover patterns existing within large scale social media networks. Though previous literature has shown that it is possible to extract customers' preferences on smartphones from Twitter data, doubts arise as whether the proposed algorithms could generalize to other product domains. In this paper, we illustrate that the methodology proposed in the previous literature could also be applied on automobile products, whose user-generated content in social media is quite limited, compared to more main stream products such as smartphones.
AB - Social media enables ubiquitous communication that allows users to disseminate and receive information anywhere and anytime. Among this increasingly vast pool of social media data reside opinionate messages that infer user experience on product usages. Knowledge extracted from such messages could prove to be useful to manufacturers and designers looking to develop next generation products that better meet the needs of the market. Recent developments in machine learning algorithms make it possible to analyze and automatically discover patterns existing within large scale social media networks. Though previous literature has shown that it is possible to extract customers' preferences on smartphones from Twitter data, doubts arise as whether the proposed algorithms could generalize to other product domains. In this paper, we illustrate that the methodology proposed in the previous literature could also be applied on automobile products, whose user-generated content in social media is quite limited, compared to more main stream products such as smartphones.
UR - https://www.scopus.com/pages/publications/85016224784
UR - https://www.scopus.com/pages/publications/85016224784#tab=citedBy
U2 - 10.1109/ICSEC.2016.7859912
DO - 10.1109/ICSEC.2016.7859912
M3 - Conference contribution
AN - SCOPUS:85016224784
T3 - 20th International Computer Science and Engineering Conference: Smart Ubiquitos Computing and Knowledge, ICSEC 2016
BT - 20th International Computer Science and Engineering Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2016 through 17 December 2016
ER -