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Tag Archives: Machine Learning
The Press and Peace, Examining Iraq War Coverage in Newspapers using BERT LLMs
by Jakobe Bussey
Abstract
This study utilizes state-of-the-art BERT (Bidirectional Encoder Representations from Transformers) models to perform sentiment analysis on Wall Street Journal and New York Times articles about the Iraq War published between 2002 and 2012 and further categorize them using advanced unsupervised machine learning techniques. By utilizing statistical analysis and quartic regression models, this paper concludes that the two newspapers report on the Iraq War differently, with both exhibiting a predominantly negative-neutral tone overall. Additionally, the analysis reveals significant fluctuations in negativity from both outlets over time as the war progresses. Furthermore, this study examines the objectivity of reporting between editorial and non-editorial articles, finding that non-editorials tend to report more objectively, and the neutrality of editorials remains relatively constant while the objectivity of non-editorials fluctuates in response to war events. Finally, the paper investigates variations in sentiment across different topics, uncovering substantial variations in positive, neutral, and negative sentiments across topics and their evolution over time.
Professor Peter Arcidiacono, Faculty Advisor
JEL Codes: L8, L82, H56
Corporate Financial Distress and Bankruptcy Prediction in North American Construction Industry
By Gang Li
This paper seeks to explore the application of Altman’s bankruptcy prediction model in the construction industry by measuring its percentage accuracy on a dataset consisting of 108 bankrupt & non-bankrupt firms selected across the timeline of 1985-2013. Another main goal this paper is to explore the predictive power of an expanded variable set tailored to the construction industry and compare the results. Specifically, this measuring process is done using machine learning algorithm based on scikit-learn library that transforms a raw .csv file into clean vectorized dataset. The algorithm provides various classifiers to cross-validate the training set, which produces mixed statistics that favors neither variable set but provides insight into the reliability of the non-linear classifiers.
Advisor: Connel Fullenkamp | JEL Codes: C38, C5, G33, G34 | Tagged: Bankruptcy, Corporate, Discriminant Analysis, Distress, Machine Learning
