Living review framework for better policy design and management of hazardous waste in Australia

The significant increase in hazardous waste generation in Australia has led to the discussion over the incorporation of artificial intelligence into the hazardous waste management system. Recent studies explored the potential applications of artificial intelligence in various processes of managing waste. However, no study has examined the use of text mining in the hazardous waste management sector for the purpose of informing policymakers. This study developed a living review framework which applied supervised text classification and text mining techniques to extract knowledge using the domain literature data between 2022 and 2023. The framework employed statistical classification models trained using iterative training and the best model XGBoost achieved an F1 score of 0.87. Using a small set of 126 manually labelled global articles, XGBoost automatically predicted the labels of 678 Australian articles with high confidence. Then, keyword extraction and unsupervised topic modelling with Latent Dirichlet Allocation (LDA) were performed. Results indicated that there were 2 main research themes in Australian literature: (1) the key waste streams and (2) the resource recovery and recycling of waste. The implication of this framework would benefit the policymakers, researchers, and hazardous waste management organisations by serving as a real time guideline of the current key waste streams and research themes in the literature which allow robust knowledge to be applied to waste management and highlight where the gap in research remains.

Key point generation as an instrument for generating core statements of a political debate on Twitter

Identifying key statements in large volumes of short, user-generated texts is essential for decision-makers to quickly grasp their key content. To address this need, this research introduces a novel abstractive key point generation (KPG) approach applicable to unlabeled text corpora, using an unsupervised approach, a feature not yet seen in existing abstractive KPG methods. The proposed method uniquely combines topic modeling for unsupervised data space segmentation with abstractive summarization techniques to efficiently generate semantically representative key points from text collections. This is further enhanced by hyperparameter tuning to optimize both the topic modeling and abstractive summarization processes. The hyperparameter tuning of the topic modeling aims at making the cluster assignment more deterministic as the probabilistic nature of the process would otherwise lead to high variability in the output. The abstractive summarization process is optimized using a Davies-Bouldin Index specifically adapted to this use case, so that the generated key points more accurately reflect the characteristic properties of this cluster. In addition, our research recommends an automated evaluation that provides a quantitative complement to the traditional qualitative analysis of KPG. This method regards KPG as a specialized form of Multidocument summarization (MDS) and employs both word-based and word-embedding-based metrics for evaluation. These criteria allow for a comprehensive and nuanced analysis of the KPG output. Demonstrated through application to a political debate on Twitter, the versatility of this approach extends to various domains, such as product review analysis and survey evaluation. This research not only paves the way for innovative development in abstractive KPG methods but also sets a benchmark for their evaluation.

The blessing and curse of "no strings attached": An automated literature analysis of psychological health and non-attachmental work in the digitalization era

Amidst tremendous changes in the worlds of work in light of digitalization, non-attachmental work designs, where individuals gain income without being bound by a fixed administrative attachment to an employer, hold promises of self-actualization along with threats of insecurity. Today's technology boom and the consequent flexibility and uncertainty it brings into workers' lives may translate into inspiring growth opportunities or overloading pressure, contingent upon mental health and wellbeing impacts. This paper first provides a conceptualization of the non-attachmental work designs of the 21st century, before proceeding to an extensive mapping of literature at their intersection with psychological health. This involves a machine-learning-driven review of 1094 scientific articles using topic modeling, combined with in-depth manual content analyses and inductive-deductive cycles of pattern discovery and category building. The resulting scholarly blueprint reveals several tendencies, including a prevalence of positive psychology concepts in research on work designs with high levels of autonomy and control, contrasted with narratives of disempowerment in service- and task-based work. We note that some psychological health issues are researched with respect to specific work designs but not others, for instance neurodiversity and the role of gender in ownership-based work, self-image and digital addiction in content-based work, and ratings-induced anxiety in platform-mediated task-based work. We also find a heavy representation of 'heroic' entrepreneurs, quantitative methods, and western contexts in addition to a surprising dearth of analyses on the roles of policy and technological interventions. The results are positioned to guide academics, decision-makers, technologists, and workers in the pursuit of healthier work designs for a more sustainable future.

Mental health concerns precede quits: shifts in the work discourse during the Covid-19 pandemic and great resignation

To study the causes of the 2021 Great Resignation, we use text analysis and investigate the changes in work- and quit-related posts between 2018 and 2021 on Reddit. We find that the Reddit discourse evolution resembles the dynamics of the U.S. quit and layoff rates. Furthermore, when the COVID-19 pandemic started, conversations related to working from home, switching jobs, work-related distress, and mental health increased, while discussions on commuting or moving for a job decreased. We distinguish between general work-related and specific quit-related discourse changes using a difference-in-differences method. Our main finding is that mental health and work-related distress topics disproportionally increased among quit-related posts since the onset of the pandemic, likely contributing to the quits of the Great Resignation. Along with better labor market conditions, some relief came beginning-to-mid-2021 when these concerns decreased. Our study underscores the importance of having access to data from online forums, such as Reddit, to study emerging economic phenomena in real time, providing a valuable supplement to traditional labor market surveys and administrative data.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-023-00417-2.

Framing the virus: The political, economic, biomedical and social understandings of the COVID-19 in Taiwan

The purpose of this paper is to study how people use texts and languages to interpret or make sense of the COVID-19 pandemic. We draw on the theoretical literature of framing perspectives to formulate our arguments that consider the virus a socially constructed reality. We use Taiwan as an empirical case study, using topic modeling analysis of newspaper articles. Our findings show that the language of the COVID-19 coverage combines the four frames of political evaluation, economic impact, biomedical science and social life in varying proportions. These frames are subject to changes in pandemic conditions. Implications for theory and practice are presented.

Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Machine learning (ML) enables the analysis of large datasets for pattern discovery. ML methods and the standards for their use have recently attracted increasing attention in organizational research; recent accounts have raised awareness of the importance of transparent ML reporting practices, especially considering the influence of preprocessing and algorithm choice on analytical results. However, efforts made thus far to advance the quality of ML research have failed to consider the special methodological requirements of unsupervised machine learning (UML) separate from the more common supervised machine learning (SML). We confronted these issues by studying a common organizational research dataset of unstructured text and discovered interpretability and representativeness trade-offs between combinations of preprocessing and UML algorithm choices that jeopardize research reproducibility, accountability, and transparency. We highlight the need for contextual justifications to address such issues and offer principles for assessing the contextual suitability of UML choices in research settings.

“When asked what I do, I say: ‘I write’”: a systematic text analysis of Peter Drucker’s writings

Purpose

A lot has been discussed about Peter Drucker, and there exists significant written content admiring or criticizing his work as a management writer. This paper aims to offer a holistic analysis of Peter Drucker’s written contributions to better understand his views of society, government and organizations of all kinds.

Design/methodology/approach

Many have written about Peter Drucker and his considerable impact on the practical and philosophical foundations of modern management. Yet, there has been no systematic scholarly evaluation of Drucker as a writer, although many have praised and criticized his written work on management. In this study, the authors offer an analysis of Peter Drucker’s written contributions to evaluate his central contributions, as well as how he communicated his ideas on society and management.

Findings

A comprehensive analysis of Drucker’s word usage and writing style throughout his writing career forms an evidence-based approach to better understand his viewpoints and objectively evaluate the criticisms surrounding his work.

Originality/value

This research contributes to a better understanding of Peter Drucker’s central contributions, concerns and sentiments, as it relates to not only business management but also to his views of society, government and organizations of all kinds. A reconsideration of Drucker as a writer presents possible implications for the practice of management.

The Effect of Positive Management Practices on Firm Profitability – Evidence from Text Mining

The concepts of positive psychology and positive organizational scholarship emphasize the value of employees’ positive emotions and satisfaction for organizations to thrive. However, conceptions of positive practices or virtuousness emanating from managers are not yet fully established and suffer from incompleteness and inconsistency. This study establishes a framework that holistically and coherently captures management practices that lead to satisfaction among employees, applying text mining and unsupervised machine learning methods to a large sample of employee reviews (n = 5,650). The framework of positive management practices (PMP) encompasses the six dimensions of respectful interaction, open communication, interpersonal support, reasonable instructions, intellectual support, and managerial competence. In a subsequent analysis, this study finds a positive association between PMP at the organizational level and firm profitability, indicating that companies should ensure that employees in leadership positions understand and adopt PMP.

Using media reports to analyze the spatio-temporal evolution of carbon dioxide management development in China

Over the past few decades, the carbon dioxide (CO2) emissions management problem has attracted global attention. China is transitioning to carbon neutrality and experienced rapid development in low-carbon management. However, current studies have limited understanding of the evolutionary process and development issues at a macro-level, which may hinder the structural reformation of stepwise carbon-neutral development. This study used the content analysis method to process and code reports from China’s most prominent news media, Xinhua News Agency, to identify China’s low-carbon evolution and development issues. The results depict a trend of gradually increasing carbon management within China and highlight the staged development features. Years 2010 and 2021 are the critical nodes of carbon emissions management in China, representing the two primary actions of low-carbon pilot city projects and the carbon-neutral construction. However, the results also reveal the uneven development problem of China’s carbon management behind the rapid transition. The government is the primary participant in carbon management, but the participation of firms and the public is relatively low. The power industry implements the highest amount of carbon management actions, but less attention is paid to other sectors with high carbon emissions. Report tones on environmental protection and green technology have gradually declined, while the tone on economic and social development has increased. There are evident differences in the number of carbon management measures implemented between regions. The southeast coastal regions report more management numbers than China’s central and western regions. The top three provinces (or municipalities) are Beijing (131), Shanghai (93), and Guangdong (78). From an industry perspective, more-reported regions have implemented carbon management measures in more industries than less-reported regions. This study provides a distinctive contribution to the theoretical work on China’s carbon emissions regulation and the emerging planning and management mechanisms.

Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media

Currently, with the dramatic increase in social media users and the greater variety of online product information, manual processing of this information is time-consuming and labour-intensive. Therefore, based on the text mining of online information, this paper analyzes the text representation method of online information, discusses the long short-term memory network, and constructs an interactive attention graph convolutional network (IAGCN) model based on graph convolutional neural network (GCNN) and attention mechanism to study the multimodal sentiment analysis (MSA) of online product information. The results show that the IAGCN model improves the accuracy by 4.78% and the F1 value by 29.25% compared with the pure interactive attention network. Meanwhile, it is found that the performance of the model is optimal when the GCNN is two layers and uses syntactic position attention. This research has important practical significance for MSA of online product information in social media.

Determining the Intensity of Basic Emotions among People Suffering from Anorexia Nervosa Based on Free Statements about Their Body

Objective: This study sought to address one of the challenges of psychiatry-computer aided diagnosis and therapy of anorexia nervosa. The goal of the paper is to present a method of determining the intensity of five emotions (happiness, sadness, anxiety, anger and disgust) in medical notes, which was then used to analyze the feelings of people suffering from anorexia nervosa. In total, 96 notes were researched (46 from people suffering from anorexia and 52 from healthy people). Method: The developed solution allows a comprehensive assessment of the intensity of five feelings (happiness, sadness, anxiety, anger and disgust) occurring in text notes. This method implements Nencki Affective Word List dictionary extension, in which the original version has a limited vocabulary. The method was tested on a group of patients suffering from anorexia nervosa and a control group (healthy people without an eating disorder). Of the analyzed medical, only 8% of the words are in the original dictionary. Results: As a result of the study, two emotional profiles were obtained: one pattern for a healthy person and one for a person suffering from anorexia nervosa. Comparing the average emotional intensity in profiles of a healthy person and person with a disorder, a higher value of happiness intensity is noticeable in the profile of a healthy person than in the profile of a person with an illness. The opposite situation occurs with other emotions (sadness, anxiety, disgust, anger); they reach higher values in the case of the profile of a person suffering from anorexia nervosa. Discussion: The presented method can be used when observing the patient’s progress during applied therapy. It allows us to state whether the chosen method has a positive effect on the mental state of the patient, and if his emotional profile is similar to the emotional profile of a healthy person. The method can also be used during first diagnosis visit.

The Multimodal Sentiment Analysis of Online Product Marketing Information Using Text Mining and Big Data

Currently, the internet is increasingly popular. More people are used to sharing their feelings about various things on the internet. Online product marketing information is also growing. How to mine the required information from the massive information with the support of big data technology has become a big problem. Thereby, based on the text mining of online product marketing information, this work discusses the text preprocessing methods and the temporal convolution network (TCN) based on a convolutional neural network (CNN). Moreover, on this basis, multimodal attention mechanism (AM) and cross-modal transformer structure are added to build a TCN based on AM (AM-TCN) model to analyze the multimodal emotion of online product marketing information. The results show that the accuracy of the AM-TCN model is 2.88% higher than that of the TCN model alone, and F1 is 3.47% higher. Moreover, the accuracy rate of the AM-TCN is 1.22% higher than that of the next highest recurrent multistage fusion network, and the F1 value is 0.95% higher.