R Code for Bloomberg News Word Cloud and Histogram

2021 ◽  
pp. 338-339
Keyword(s):  
Word Cloud

User password repetitive patterns analysis and visualization

2016 ◽  
Vol 24 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Xiaoying Yu ◽  
Qi Liao
Keyword(s):  
Security Policy ◽  
Large Data ◽  
Efficient Algorithms ◽  
Privacy And Security ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Word Cloud ◽  
Individual Privacy ◽  
Password Security

Purpose – Passwords have been designed to protect individual privacy and security and widely used in almost every area of our life. The strength of passwords is therefore critical to the security of our systems. However, due to the explosion of user accounts and increasing complexity of password rules, users are struggling to find ways to make up sufficiently secure yet easy-to-remember passwords. This paper aims to investigate whether there are repetitive patterns when users choose passwords and how such behaviors may affect us to rethink password security policy. Design/methodology/approach – The authors develop a model to formalize the password repetitive problem and design efficient algorithms to analyze the repeat patterns. To help security practitioners to analyze patterns, the authors design and implement a lightweight, Web-based visualization tool for interactive exploration of password data. Findings – Through case studies on a real-world leaked password data set, the authors demonstrate how the tool can be used to identify various interesting patterns, e.g. shorter substrings of the same type used to make up longer strings, which are then repeated to make up the final passwords, suggesting that the length requirement of password policy does not necessarily increase security. Originality/value – The contributions of this study are two-fold. First, the authors formalize the problem of password repetitive patterns by considering both short and long substrings and in both directions, which have not yet been considered in past. Efficient algorithms are developed and implemented that can analyze various repeat patterns quickly even in large data set. Second, the authors design and implement four novel visualization views that are particularly useful for exploration of password repeat patterns, i.e. the character frequency charts view, the short repeat heatmap view, the long repeat parallel coordinates view and the repeat word cloud view.

scholarly journals Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach

2020 ◽  
Vol 12 (11) ◽  
pp. 4753
Author(s):  
Viju Raghupathi ◽  
Jie Ren ◽  
Wullianallur Raghupathi
Keyword(s):  
Machine Learning ◽  
Corporate Sustainability ◽  
Primary Source ◽  
Text Analytics ◽  
Useful Knowledge ◽  
Word Cloud ◽  
Textual Data ◽  
Shareholder Resolution ◽  
The Impact ◽  
Insight Into

Corporations have embraced the idea of corporate environmental, social, and governance (ESG) under the general framework of sustainability. Studies have measured and analyzed the impact of internal sustainability efforts on the performance of individual companies, policies, and projects. This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics. Prior research has studied corporate sustainability disclosures from public reports. By studying shareholder resolutions, we gain insight into the shareholders’ perspectives and objectives. The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019. The study utilizes a combination of text analytic approaches (i.e., word cloud, co-occurrence, row-similarities, clustering, classification, etc.) to extract insights. These are novel methods of transforming textual data into useful knowledge about corporate sustainability endeavors. This study demonstrates that stakeholders, such as shareholders, can influence corporate sustainability via resolutions. The incorporation of text analytic techniques offers insight to researchers who study vast collections of unstructured bodies of text, improving the understanding of shareholder resolutions and reaching a wider audience.

scholarly journals P237 The Word Cloud: A Visual Legacy of a Loved One's Life

2016 ◽  
Vol 52 (6) ◽  
pp. e128-e129
Author(s):  
Feli Toledo ◽  
Marilyn Swinton ◽  
France Clarke ◽  
Lois Saunders ◽  
Anne Woods ◽  
...  
Keyword(s):  
Word Cloud

scholarly journals Factors underlying treatment decision-making for localized prostate cancer in the U.S. and Canada: A scoping review using principal component analysis

2018 ◽  
Vol 13 (7) ◽  
Author(s):  
Mustafa Andkhoie ◽  
Desneige Meyer ◽  
Michael Szafron
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Principal Component Analysis ◽  
Scoping Review ◽  
Principal Component ◽  
Treatment Decision ◽  
Localized Prostate Cancer ◽  
Key Factors ◽  
Treatment Decision Making ◽  
Word Cloud

Introduction: The purpose of this research is to gather, collate, and identify key factors commonly studied in localized prostate cancer (LPC) treatment decision-making in Canada and the U.S.Methods: This scoping review uses five databases (Medline, EMBASE, CINAHL, AMED, and PsycInfo) to identify relevant articles using a list of inclusion and exclusion criteria applied by two reviewers. A list of topics describing the themes of the articles was extracted and key factors were identified using principal component analysis (PCA). A word cloud of titles and abstracts of the relevant articles was created to identify complementary results to the PCA.Results: This review identified 77 relevant articles describing 32 topics related to LPC treatment decision-making. The PCA grouped these 32 topics into five key factors commonly studied in LPC treatment decision-making: 1) treatment type; 2) socioeconomic/demographic characteristics; 3) personal reasons for treatment choice; 4) psychology of treatment decision experience; and 5) level of involvement in the decision-making process. The word cloud identified common phrases that were complementary to the factors identified through the PCA.Conclusions: This research identifies several possible factors impacting LPC treatment decision-making. Further research needs to be completed to determine the impact that these factors have in the LPC treatment decision-making experience.

scholarly journals Displaying Labeled Quantitative Data

2019 ◽  
Vol 5 (2) ◽  
pp. 11-20
Author(s):  
Marcin Kozak
Keyword(s):  
Quantitative Data ◽  
Visual Representation ◽  
Graphical Representations ◽  
Data Set ◽  
Quantitative Variable ◽  
Word Cloud

Abstract The information world is full of labeled quantitative data, in which a number of qualitative categories are to be compared based on a quantitative variable. Their graphical representations are various and serve different audiences and purposes. Based on a simple data set and its different visualizations, we will play with the data and their visual representation. We will use well-known charts, such as a regular table, a bar plot, and a word cloud; less-know, such as Cleveland’s dot plot, a fan plot, and a text-table; and new ones, constructed for the very aim of this essay, such as a labeled rectangle plot and a ruler-like graph. Our discussion will not aim to choose the best graph but rather to show the different faces of visualizing labeled quantitative data. I hope to convince the readers that it is always worth spending a minute on pondering how to present their data.

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