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"Samuel, Jim"
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COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
Journal Article
Online gambling forums as a potential target for harm reduction: an exploratory natural language processing analysis of a reddit.com forum
Objectives
Globally, there has been a rapid increase in the availability of online gambling. As online gambling has increased in popularity, there has been a corresponding increase in online communities that discuss gambling. The movement of gambling and communities interested in gambling to online spaces presents new challenges to harm reduction. The current study analyses a forum from a popular online forum hosting website (reddit.com) to determine its suitability as a source for data to inform gambling harm reduction in online spaces.
Methods
The current study provides an exploratory analysis of 1,141 unique posts and 11,668 comments collected from the online forum r/onlinegambling. The dataset covers posts and comments from August 5, 2015, to October 30, 2023. Natural language processing (NLP) techniques were used to identify common terms and phrases, identify topics with high rates of participant engagement and perform a sentiment analysis of posts and comments.
Results
Sentiment analysis results showed that the majority of posts and comments were positive, but there were substantial numbers of negative and neutral content. Positive content was often congratulatory and focused on winning, neutral posts more commonly focused on practical advice, and negative posts were more commonly concerned with avoiding operators perceived as illegitimate by forum participants.
Conclusions
Results from this study show that there is a varied and robust discussion of different aspects of gambling on r/onlinegambling. Our exploratory analyses suggest that this reddit forum provides important information on how users communicate motivations to gamble, interpretations of gambling experiences, and define potential harms related to gambling online as well as how to avoid or remedy those harms. Reddit forums discussing gambling have great potential for future research interested in more specific aspects of harm reduction and prevention related to online gambling, particularly when using NLP techniques.
Journal Article
Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods
Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differences in the writing style of alphabets and digits. This study aims to discuss the steps needed to create an adaptive framework for OCR models, with the intent of exploring a reasonable method to customize an OCR solution for a unique dataset of English language numerical digits were developed for this study. We develop a digit recognizer by training our model on the MNIST dataset with a convolutional neural network and contrast it with multiple models trained on combinations of the MNIST and custom digits. Using our methods, we observed results comparable with the baseline and provided recommendations for improving OCR accuracy for localized or personalized handwritten text. This study also provides an alternative perspective to generating data using conventional methods, which can serve as a gold standard for custom data augmentation to help address the challenges of scarce data and data imbalance.
Journal Article
Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency
Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train healthcare professionals, and advance medical research. This paper examines various clinical and non-clinical applications of Gen AI. In clinical settings, Gen AI supports the creation of customized treatment plans, generation of synthetic data, analysis of medical images, nursing workflow management, risk prediction, pandemic preparedness, and population health management. By automating administrative tasks such as medical documentations, Gen AI has the potential to reduce clinician burnout, freeing more time for direct patient care. Furthermore, application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms. The generation of synthetic data opens new avenues for model training for diseases and simulation, enhancing research capabilities and improving predictive accuracy. In non-clinical contexts, Gen AI improves medical education, public relations, revenue cycle management, healthcare marketing etc. Its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.
Journal Article
International Tax Planning: The Preacquisition Surplus Election: More Than Meets the Eye?
Regulation 5901(2)(b) permits a corporation resident in Canada (CRIC) to make an election in respect of a dividend paid by a foreign affiliate of the CRIC such that the dividend will be treated as a reduction to the adjusted cost base of the foreign affiliate shares on which it is paid, rather than as a distribution from the foreign affiliate's surplus pools. Making this \"preacquisition surplus election\" is often perceived as a straightforward, and administratively simple, way for the CRIC to ensure that the dividend does not result in the unwanted, and potentially adverse, distribution of the foreign affiliate's surplus pools. However, as this article points out, it is important that a CRIC undertake a detailed analysis before deciding to make the election, in order to avoid potential exposure to unintended consequences. This article provides an overview of the legislative history of the election and its underlying policy rationale, and describes the limitations of and restrictions on its use. The authors present some conceptual guidelines (along with examples of their application) for identifying certain circumstances in which the making of the election may be particularly favourable or unfavourable. They also compare and contrast this election with the \"qualifying return of capital\" (QROC) election under subsection 90(3), which may be available in some circumstances as an alternative mechanism for achieving a similar result.
Journal Article
Interaction of the Foreign Affiliate Surplus and Safe-Income Regimes: Selected Anomalies, Issues, and Planning Considerations
Le gouvernement canadien a présente, dans son budget de 2015, des modifications substantielles â l'article 55 de la Loi de l'impôt sur le revenu. Ces modifications comprennent deux nouveaux criteres de l'objet qui permettent d'établir si le paragraphe 55(2) s'applique pour requalifier un dividende intersociétés autrement libre d'impôt lorsqu'il est versé par une société qui réside au Canada â une autre société résidente en tant que gain en capital qui est assujetti â l'impôt. Étant donné la large portée de ces nouveaux criteres de l'objet, et de l'incertitude qu'ils créent, il sera â présent probablement plus courant pour les sociétés de se prévaloir, lorsque c'est possible, de l'exception pour revenu protégé. Ces circonstances pourraient inclure, par exemple, le paiement d'un dividende intersociétés par une filiale canadienne détenue en propriété exclusive â sa société mere. En outre, si cette filiale détient, directement ou indirectement, une ou plusieurs sociétés étrangeres affiliées, il est possible que la totalité ou une partie des surplus mis en commun d'une ou de plusieurs de ces sociétés affiliées puisse faire partie intégrante, dans certaines circonstances, du calcul d'un revenu protégé. Cet article présente une comparaison des aspects fondamentaux de ces deux régimes, en mettant particulierement l'accent sur les anomalies, les questions et les considérations de planification les plus courantes dans le contexte de leur interaction. L'analyse présentée par l'auteur montre que, bien que les deux régimes aient un objectif de calcul similaire, l'interaction des deux peut parfois etre difficile et produire des résultats imprévus.
Journal Article
THE PREACQUISITION SURPLUS ELECTION: MORE THAN MEETS THE EYE?
Regulation 5901(2)(b) permits a corporation resident in Canada (CRIC) to make an election in respect of a dividend paid by a foreign affiliate of the CRIC such that the dividend will be treated as a reduction to the adjusted cost base of the foreign affiliate shares on which it is paid, rather than as a distribution from the foreign affiliates surplus pools. Making this preacquisition surplus election is often perceived as a straightforward, and administratively simple, way for the CRIC to ensure that the dividend does not result in the unwanted, and potentially adverse, distribution of the foreign affiliates surplus pools. However, as this article points out, it is important that a CRIC undertake a detailed analysis before deciding to make the election, in order to avoid potential exposure to unintended consequences. This article provides an overview of the legislative history of the election and its underlying policy rationale, and describes the limitations of and restrictions on its use. The authors present some conceptual guidelines (along with examples of their application) for identifying certain circumstances in which the making of the election may be particularly favourable or unfavourable. They also compare and contrast this election with the qualifying return of capital (QROC) election under subsection 90(3), which may be available in some circumstances as an alternative mechanism for achieving a similar result.
Journal Article
Information Token Driven Machine Learning For Electronic Markets: Performance Effects In Behavioral Financial Big Data Analytics
Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of ‘big-data’ phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned: ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions.
Journal Article