Term of Award

Summer 2018

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (restricted to Georgia Southern)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Committee Chair

Jeffrey Kaleta

Committee Member 1

Cheryl Aasheim

Committee Member 2

Adrian Gardiner


Online reviews are gaining popularity on online e-commerce sites and extend electronic word of mouth in the form of consumer reviews. Reviews provided by consumers can influence the sale of products as a means to which online shoppers use the related information for decision making towards a purchase. This study looks at online reviews of digital cameras, a feature rich good, evaluating how the contents of the review influences the reviewers overall rating of the product (1 – 5 stars) and potentially how helpful that review is to online shoppers. Furthermore, this study explores the contents of the review by analyzing the sentiment of the text, the topics within the text, and ultimately investigate how the reviewer represents these attributes. To do so, sentiment analysis of the reviews is performed, then to determine the significant variable that influence overall ratings and helpfulness, a linear regression is conducted. Using these variables, the star rating for each review is predicted. Finally a topic mining model is implemented to find the frequent topics in the reviews that belong to each ratings.

Research Data and Supplementary Material