Journal of Big Data: Theory and Practice最新文献

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Global Perception of the Belt and Road Initiative: A Natural Language Processing Approach “一带一路”倡议的全球感知:一种自然语言处理方法

Journal of Big Data: Theory and Practice Pub Date : 2022-06-28 DOI: 10.54116/jbdtp.v1i1.18

Amit A. Mokashi, J. Jayaraman, Priyanka Mahakul, Rutu Patel, Anthony G. Picciano

引用次数: 3

In search of pedagogical approaches to teaching business ethics in the era of digital transformation 寻找数字化转型时代商业伦理教学方法

Journal of Big Data: Theory and Practice Pub Date : 2022-06-28 DOI: 10.54116/jbdtp.v1i1.17

Ethné M. Swartz, M. Brennan-Tonetta, Rashmi Jain, Marin Johnson, Stanislav Mamanov, M. Hale, J. Jayaraman

{"title":"In search of pedagogical approaches to teaching business ethics in the era of digital transformation","authors":"Ethné M. Swartz, M. Brennan-Tonetta, Rashmi Jain, Marin Johnson, Stanislav Mamanov, M. Hale, J. Jayaraman","doi":"10.54116/jbdtp.v1i1.17","DOIUrl":"https://doi.org/10.54116/jbdtp.v1i1.17","url":null,"abstract":"The authors explore the challenges in teaching business ethics in an era of digital transformation, provide an understanding of the limitations of traditional ethics approaches, and explore emerging approaches that may more effectively deal with the ethical complexities of the new digital era. Building on prior research conducted during December 2018 and January 2019 regarding the skills required for jobs in the big data field, the authors argue that business ethics must be an essential skill for those working in this field. Ethical frameworks in big data and information management are explored including Universalist, Integrative Social Contract Theory and Care Theory, as well as agency, disciplinary, contextual and outcomes considerations. The authors posit that traditional ethical frameworks such as Universalist approaches, are no longer sufficient to guide decision making in an era of digital transformation and the “datafication” of society. Therefore, business educators have a duty to cultivate critical thinking mindsets in their students and the adoption of “responsible innovation” principles, similar to those developed in the science and technology innovation literature. \u0000 \u0000 ","PeriodicalId":216885,"journal":{"name":"Journal of Big Data: Theory and Practice","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116140983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Four-Class Emotion Classification Problem using Deep Learning Classifiers 基于深度学习分类器的四类情绪分类问题

Journal of Big Data: Theory and Practice Pub Date : 2022-06-28 DOI: 10.54116/jbdtp.v1i1.19

Miaojie Zhou, Abhishek Tripathi, S. M. Srinivasan

{"title":"Four-Class Emotion Classification Problem using Deep Learning Classifiers","authors":"Miaojie Zhou, Abhishek Tripathi, S. M. Srinivasan","doi":"10.54116/jbdtp.v1i1.19","DOIUrl":"https://doi.org/10.54116/jbdtp.v1i1.19","url":null,"abstract":"Social media sites and blogs generate a vast amount of emotionally rich data in the form of tweets, status updates, blog posts etc. Such textual data are a good representative of emotions expressed by an individual or a group of people on any given topic. By analyzing the emotions within these textual data, we can get an idea about how an individual or a community expresses their views. Analytical techniques are widely used for analyzing emotions within these texts. However, due to the imbalanced nature of the training datasets the supervised classifiers fail to clearly classify the different emotion classes. As a result, these classifiers demonstrate a poor performance in identifying emotions within the texts. Here, using a constructed heterogeneous training dataset from well-known training datasets we have trained two deep learning models namely the Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) to address a four-class emotion (Anger, Sadness, Happy, Surprise) classification problem. By appropriately tuning the hyper parameters of the deep learning classifiers our study reveals that the CNN classifier has a slightly better performance (77%) than the RNN classifier (76%) for a four-class emotion classification problem.","PeriodicalId":216885,"journal":{"name":"Journal of Big Data: Theory and Practice","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121633362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0