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CC BY-NC-ND V4.0, Open Access
Dissertation
BUSINESS ECONOMICS OF ARTIFICIAL INTELLIGENCE (AI) AND BIG-DATA
Washington State University
Doctor of Philosophy (PhD), Washington State University
01/2021
DOI:
https://doi.org/10.7273/000002472
Handle:
https://hdl.handle.net/2376/121328
Abstract
This dissertation consists of three papers: (1) applications of interpretable machine learning and deep learning for identifying latent consumer preferences from user-generated text data, (2) classification of biased reviews, and (3) intraday US stock market risk prediction. The first paper demonstrates how to identify and predict latent consumer preferences for programmable thermostats from user-generated content (i.e., online product reviews) on Amazon.com by leveraging discrete choice modeling, machine learning, and natural language processing. The second paper shows how to identify and classify ‘always the same raters’ in a one-sided-review system (Amazon.com) using big data analytics, discrete choice modeling, and natural language processing. The third paper shows how to predict the US stock market risk in the beginning hour of trading by using news in the after-market period and financial indicators. Therefore, the findings of this dissertation may help industry leaders and policymakers understand how to apply artificial intelligence (AI) and big data with a combination of conventional structured data and unstructured data for business.
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Details
- Title
- BUSINESS ECONOMICS OF ARTIFICIAL INTELLIGENCE (AI) AND BIG-DATA
- Creators
- Jikhan Jeong
- Contributors
- H. Alan Love (Advisor)Jinhui Bai (Committee Member)Bidisha Mandal (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Economic Sciences, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 177
- Identifiers
- 99900606955701842
- Language
- English
- Resource Type
- Dissertation