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Essays on retail operations and the recent pandemic (COVID-19): using mathematical and text-mining approaches
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Title
Essays on retail operations and the recent pandemic (COVID-19): using mathematical and text-mining approaches
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Mahdikhani
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Maryam
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1987
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maryam mahdikhani
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author
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Zhao
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Yao
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Yao Zhao
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Advisory Committee
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chair
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Yang
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Jian
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Jian Yang
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internal member
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Rodgers
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Mark Rodgers
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Cho
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Soo Hyun
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Soo Hyun Cho
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Zhao
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Shaoqiong
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Shaoqiong Zhao
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Advisory Committee
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Rutgers University
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Graduate School - Newark
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theses
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2020
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2020-10
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English
Abstract
This dissertation consists of three essays. The first essay examines on auction design and the last two essays apply sentiment analysis methodologies on big data.
The first paper of my dissertation examines the auction design with negative externality and its impact on the optimal mechanism design. In light of previous studies, our research shows that auctioning a good may impact the seller's payoff and those who lose the object. We simplify the potential mechanism by depriving buyers of their right to absolute non-participation. Our characterizations are thus tailored towards understanding bidders' type space, and the information structure of single-object auctions with negative externality's set up.
The second paper of my dissertation aims to predict helpful reviews on Amazon Fashion products and identify the most frequent terms in such reviews. We choose features from topics using the latent Dirichlet allocation (LDA) model and topics plus Bi-grams using the TF-IDF vectorizer. We then use the features to enhance the performance of support vector machine (SVM) classifier to predict the helpfulness of reviews. The research is performed on a large corpus of Amazon fashion reviews. We find that reviews gets more votes when they are more specific regarding quality of product and return experience.
The third essay of my dissertation is motivated by tweets on COVID-19 and the retweeting behavior. Our research objective is to predict tweet's popularity based on the volume of retweets regardless of the user's followers. We examine the features selection, including (i) topics by using LDA, (ii) N-grams by using TF-IDF vectorizer, and (iii) topics plus Bi-grams TF-IDF vectorizer. We use the extracted features on Random Forest (RF) classifier, SVM classifier, and Logistic Regression (LR) classifier. We find that RF has the highest accuracy for predicting the volume of retweets by particularly using topics plus Bi-grams TF-IDF vectorizer.
The first paper of my dissertation examines the auction design with negative externality and its impact on the optimal mechanism design. In light of previous studies, our research shows that auctioning a good may impact the seller's payoff and those who lose the object. We simplify the potential mechanism by depriving buyers of their right to absolute non-participation. Our characterizations are thus tailored towards understanding bidders' type space, and the information structure of single-object auctions with negative externality's set up.
The second paper of my dissertation aims to predict helpful reviews on Amazon Fashion products and identify the most frequent terms in such reviews. We choose features from topics using the latent Dirichlet allocation (LDA) model and topics plus Bi-grams using the TF-IDF vectorizer. We then use the features to enhance the performance of support vector machine (SVM) classifier to predict the helpfulness of reviews. The research is performed on a large corpus of Amazon fashion reviews. We find that reviews gets more votes when they are more specific regarding quality of product and return experience.
The third essay of my dissertation is motivated by tweets on COVID-19 and the retweeting behavior. Our research objective is to predict tweet's popularity based on the volume of retweets regardless of the user's followers. We examine the features selection, including (i) topics by using LDA, (ii) N-grams by using TF-IDF vectorizer, and (iii) topics plus Bi-grams TF-IDF vectorizer. We use the extracted features on Random Forest (RF) classifier, SVM classifier, and Logistic Regression (LR) classifier. We find that RF has the highest accuracy for predicting the volume of retweets by particularly using topics plus Bi-grams TF-IDF vectorizer.
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Retail operations
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American Studies
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Rutgers University Electronic Theses and Dissertations
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1 online resource (xii, 134 pages)
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Ph.D.
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Includes bibliographical references
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External ETD doctoral
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Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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doi:10.7282/t3-66as-ts56
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The author owns the copyright to this work.
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mahdikhani
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maryam
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2020-09-07 16:07:03
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maryam mahdikhani
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Rutgers University. Graduate School - Newark
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2020-10-31
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2022-10-31
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Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2022.
Copyright
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Copyright protected
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Open
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Permission or license
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