Data Driven Domain Appraisal: Extracting Information from Short Dense Texts
Jian Liu, Xiangdong Zeng, Adam Ghandar, Georgios K. Theodoropoulos · 2019
Domain names can be traded via online market places and auctions. Speculation can be lucrative, with high value transactions reaching into tens of millions of dollars. This paper proposes a framework for automated domain name appraisal and evaluates several formulations of the problem with real world data. A dynamic nonlinear valuation modelling process is defined using machine learning techniques. Attributes or value factors are derived from short domain name text strings as well as a variety of other contextual data able to be obtained online from open sources. A data set of 9.975 million domains is used for evaluation and results show that search engine query data is a primary driver of value but using extracted text features can facilitate higher performance in distinguishing high value domains particularly when used with ensemble learning.