Abstract

Understanding competition between businesses is essential for assessing the likely success of new ventures or products, for making decisions before investing capital in new businesses, and understanding the impacts of regulatory policy. One important resource for analyzing competitor relationships are business webpages, which can capture the mission, products, services, and key markets associated with a company. However, webpages also contain irrelevant, extraneous, or misleading text, hampering prediction. To address this challenge, predictive models use a process known as feature selection to identify only relevant terms. The diversity and specificity of business domains pose a challenge for automated approaches for feature selection. In this paper, we compare two approaches to feature selection: manually-curated lists of terms provided by experts and automated approaches to feature selection. We …

Date

June 15, 2018

Authors

Rahul Gupta, Jay Pujara, Craig A Knoblock, Shushyam M Sharanappa, Bharat Pulavarti, Gerard Hoberg, Gordon Phillips

Book

Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets

Pages

1-6