Automated discovery of product preferences in ubiquitous social media data: A Case study of automobile market

Suppawong Tuarob, Conrad S. Tucker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication20th International Computer Science and Engineering Conference
Subtitle of host publicationSmart Ubiquitos Computing and Knowledge, ICSEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509044207
DOIs
StatePublished - Feb 21 2017
Event20th International Computer Science and Engineering Conference, ICSEC 2016 - Chiang Mai, Thailand
Duration: Dec 14 2016Dec 17 2016

Publication series

Name20th International Computer Science and Engineering Conference: Smart Ubiquitos Computing and Knowledge, ICSEC 2016

Other

Other20th International Computer Science and Engineering Conference, ICSEC 2016
CountryThailand
CityChiang Mai
Period12/14/1612/17/16

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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