Doctor of Philosophy, The Ohio State University, 2017, Industrial and Systems Engineering

Many decision problems are set in changing environments. For example, determining the optimal investment in cyber maintenance depends on whether there is evidence of an unusual vulnerability such as “Heartbleed” that is causing an especially high rate of incidents. This gives rise to the need for timely information to update decision models so that the optimal policies can be generated for each decision period. Social media provides a streaming source of relevant information, but that information needs to be efficiently transformed into numbers to enable the needed updates. This dissertation first explores the use of social media as an observation source for timely decision-making. To efficiently generate the observations for Bayesian updates, the dissertation proposes a novel computational method to fit an existing clustering model, called K-means Latent Dirichlet Allocation (KLDA). The method is illustrated using a cyber security problem related to changing maintenance policies during periods of elevated risk. Also, the dissertation studies four text corpora with 100 replications and show that KLDA is associated with significantly reduced computational times and more consistent model accuracy compared with collapsed Gibbs sampling. Because social media is becoming more popular, researchers have begun applying text analytics models and tools to extract information from these social media platforms. Many of the text analytics models are based on Latent Dirichlet Allocation (LDA). But these models are often poor estimators of topic proportions for emerging topics. Therefore, the second part of dissertation proposes a visual summarizing technique based on topic models, a point system, and Twitter feeds to support passive summarizing and sensemaking. The associated “importance score” point system is intended to mitigate the weakness of topic models. The proposed method is called TWitter Importance Score Topic (TWIST) summarizing method. TWIST employs the topic propor (open full item for complete abstract)

Committee: Theodore Allen (Advisor); Steven MacEachern (Committee Member); Cathy Xia (Committee Member); Nena Couch (Other) Subjects: Finance; Industrial Engineering; Operations Research; Statistics; Systems Science

Master of Science (MS), Ohio University, 2015, Industrial and Systems Engineering (Engineering and Technology)

As part of planning for the future and retirement, people typically build their investment portfolio. Investment portfolios are made up of four different asset classes, and typically managed by one of the major investment firms such as the Edward Jones Company. This research works with artificial neural networks (ANN) and closely with an advisor from the Edward Jones Company to provide a machine learning decision making aid for them to use when allocating the four main asset classes that make up a portfolio. The asset class prediction results and trends are then compared by the advisors consulted to decide if this methodology would be a useful aid during high volatility times in the stock market, such as the market crash of 2008. The use of this successful machine learning aid will benefit the investment portfolio that shows promise for yielding higher return on investment (ROI). This research was determined to be a successful machine learning aid to assist advisors with the asset allocation of an investment portfolio.

Committee: Gary Weckman Ph.D. (Advisor); Andy Snow Ph.D. (Committee Member); Tao Yuan Ph.D. (Committee Member); Namkyu Park Ph.D. (Committee Member) Subjects: Engineering; Finance