Latent Dirichlet Allocation and Predatory Pricing Online Data
Date
2021-02-24
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Applied Economics and Statistics, University of Delaware, Newark, DE.
Abstract
In this paper, we study Latent Dirichlet Allocation (LDA; Blei et al., 2012) for
topic modeling of Amazon unfair pricing data during Covid-19. A topic model
is designed to capture topics relating to words in text document or corpus. LDA
is a generative probabilistic model with helping to collect topics from discrete
data, like text & corpora. It is also known as a three-level hierarchical Bayesian
model, where each item of the collection is modeled as a nite mixture over an
underlying set of topics. For each topic, it is modeled as an in nite mixture
on an underlying set of basic topic probabilities in turn. We conduct analy-
sis of unfair pricing data by sellers from Amazon during the Covid-19 period
using LDA. Speci cally, we perform topic modeling and generate topics under
Amazon product description. Our goal is to capture information and topics on
what kind of surgical masks and products are in unfair pricing during Covid-
19. Finally, we conclude that N95 is the most unfairly priced product under
the topic modeling. By generating graphical illustrations with the Python pyL-
DAvis package, we are able to summarize and provide more detailed information
based on Predatory Pricing Online model.
Description
Keywords
Topic modeling, Natural language processing, Latent Dirichlet Allocation, Predatory pricing online data, COVID-19