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Articles 1 - 10 of 10
Full-Text Articles in Business
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
School of Business: Faculty Publications and Other Works
Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …
Predictors Of Covid-19 Vaccination Rate In Usa: A Machine Learning Approach, Syed M. I. Osman, Ahmed Sabit
Predictors Of Covid-19 Vaccination Rate In Usa: A Machine Learning Approach, Syed M. I. Osman, Ahmed Sabit
WCBT Faculty Publications
In this study, we examine state-level features and policies that are most important in achieving a threshold level vaccination rate to curve the effects of the COVID-19 pandemic. We employ CHAID, a decision tree algorithm, on three different model specifications to answer this question based on a dataset that includes all the states in the United States. Workplace travel emerges as the most important predictor; however, the governors’ political affiliation (PA) replaces it in a more conservative feature set that includes economic features and the growth rate of COVID-19 cases. We also employ several alternative algorithms as a robustness check. …
The Impact Of Subscription Programs On Customer Purchases, Raghu Iyengar, Young-Hoon Park, Qi Yu
The Impact Of Subscription Programs On Customer Purchases, Raghu Iyengar, Young-Hoon Park, Qi Yu
Research Collection Lee Kong Chian School Of Business
Subscription programs have become increasingly popular among a wide variety of retailers and marketplace platforms. Subscription programs give members access to a set of exclusive benefits for a fixed fee upfront. In this paper, we examine the causal effect of a subscription program on customer behavior. To account for self-selection and identify the individual-level treatment effects, we combine a difference-in-differences approach with a generalized random forests procedure that matches each member of the subscription program with comparable non-members. We find subscription leads to a large increase in customer purchases. The effect of subscription is economically significant, persistent over time, and …
Understanding Sentiment Through Context, Richard M.Crowley, M.H. Franco Wong
Understanding Sentiment Through Context, Richard M.Crowley, M.H. Franco Wong
Research Collection School Of Accountancy
We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that positive and negative sentiment are driven by different contexts. We then construct context-level sentiment measures and examine whether sentiment works as expected at the context-level across four prediction problems. Our results demonstrate that document-level sentiment exhibits significant noise in prediction and suggest that document-level aggregation of sentiment leads to missed empirical nuances. The contexts driving sentiment results vary …
Causal Forest Approach For Site-Specific Input Management Via On-Farm Precision Experimentation, Shunkei Kakimoto
Causal Forest Approach For Site-Specific Input Management Via On-Farm Precision Experimentation, Shunkei Kakimoto
Department of Agricultural Economics: Dissertations, Theses, and Student Research
Estimating site-specific crop yield response to changes to input (e.g., seed, fertilizer) management is a critical step in making economically optimal site-specific input management recommendations. Past studies have attempted to estimate yield response functions using various Machine Learning (ML) methods, including the Random Forest (RF), Boosted Random Forest (BRF), and Convolutional Neural Network (CNN) methods. This study proposes use of the Causal Forest (CF) model, which is one of the emerging ML methods that comprise “Causal Machine Learning.” Unlike previous yield-prediction-oriented ML methods, CF focuses strictly on estimating heterogeneous treatment effects (changes in yields that result from changes in input …
Did Twitter Deliberately Mislead Elon Musk In His Acquisition Bid?, Mark Humphery-Jenner
Did Twitter Deliberately Mislead Elon Musk In His Acquisition Bid?, Mark Humphery-Jenner
Perspectives@SMU
Elon Musk has officially ended his bid to acquire Twitter on the grounds that it misled the market in its disclosures, writes UNSW Business School's Mark Humphery-Jenner
Studying The Executive Perception Of Investment In Intelligent Systems And The Effect On Firm Performance, Noel Romesh Wijesinha
Studying The Executive Perception Of Investment In Intelligent Systems And The Effect On Firm Performance, Noel Romesh Wijesinha
FIU Electronic Theses and Dissertations
This research was conducted to examine the relationship between investment in intelligent systems resources and capabilities (based on artificial intelligence and machine learning algorithms) and the effect on company performance. Despite existing research on the benefits of adopting intelligent systems, companies have been slow to adopt as there is lack of research on intelligent systems use cases that will increase firm performance. This research study used resource-based view (RBV) and dynamic capabilities (DCF) theory to investigate firms’ investment in intelligent systems resources that build intelligent systems capabilities and the association to organization performance dimensions, revenue and profits. To answer this …
Twitter Demonstrates Why Poison Pills Are Bad For Shareholders, Mark Humphery-Jenner
Twitter Demonstrates Why Poison Pills Are Bad For Shareholders, Mark Humphery-Jenner
Perspectives@SMU
Twitter’s poison pill appears to be an attempt to entrench the board rather than delivering shareholder value, writes UNSW Business School's Mark Humphery-Jenner
Describing Rosé: An Embedding-Based Method For Measuring Preferences, Anirban Mukherjee, Hannah H. Chang
Describing Rosé: An Embedding-Based Method For Measuring Preferences, Anirban Mukherjee, Hannah H. Chang
Research Collection Lee Kong Chian School Of Business
In this paper, we present a novel preference-measurement method for experiential products and develop a novel embedding-based utility model to value product attributes and attribute-levels from participant choices between products described in (unstructured) prose.
Integrating Machine Learning Algorithms With Quantum Annealing Solvers For Online Fraud Detection, Haibo Wang, Wendy Wang, Yi Liu, Bahram Alidaee
Integrating Machine Learning Algorithms With Quantum Annealing Solvers For Online Fraud Detection, Haibo Wang, Wendy Wang, Yi Liu, Bahram Alidaee
Faculty and Student Publications
Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems detect duplicitous activities after they have already occurred, not at or near real time. Since spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection very challenging and calls for ways to address it beyond the traditional machine learning approach. This study has proposed a detection framework, and implemented it using quantum machine learning (QML) approach by applying Support Vector Machine (SVM) enhanced with quantum annealing solvers. To evaluate its detection performance, we have further implemented twelve machine learning …