Date of Award

4-1-2023

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Kevin H. Lee, Ph.D.

Second Advisor

Joshua Naranjo, Ph.D.

Third Advisor

Hyun Bin Kang, Ph.D.

Fourth Advisor

Jinseok Kim, Ph.D.

Keywords

Amazon product review data, clustering networks, exponential random graph models, stochastic block models, topic modelling (LDA), variational em algorithms mm algorithms

Abstract

As the online market grows rapidly, many companies and researchers are interested in analyzing product review dataset which includes ratings and text review data. In the first project, we mainly focus on analyzing the text review data. In the current literature, it is common to use only text analysis tools to analyze review dataset. But in our work, we propose a method that utilizes both a text analysis method such as topic modeling and a statistical network model to build network among individuals and find interesting communities. We introduce a promising framework that incorporates topic modeling technique to define the edges among the individuals and form a network and uses stochastic Blockmodels (SBM) to find the communities.

The second project involves clustering networks leveraging covariate information. Exponential random graph models (ERGMs) offer a variety of statistical network data and applications. Finding nodes that have similar patterns is an imperative research question. Our concept of utilizing covariate information in networks is a response to the non-homogeneous situation that exists in many network data sets. To find approximate maximum likelihood estimates of network and covariates parameters, and mixing proportions, we present a variational expectation maximization algorithm. The power of our model is demonstrated in simulation studies.

Access Setting

Dissertation-Open Access

Download

Share

COinS