New frontiers for qualitative textual data analysis: a multimethod statistical approach

In recent years, the increase in textual data production has meant that researchers require faster text analysis techniques and software to reliably produce knowledge for the scientific-nursing community. Automatic text data analysis opens the frontiers to a new research area combining the depth of analysis typical of qualitative research and the stability of measurements required for quantitative studies. Thanks to the statistical-computational approach, it proposes to study more or less extensive written texts produced in natural language to reveal lexical and linguistic worlds and extract useful and meaningful information for researchers. This article aims to provide an overview of this methodology, which has been rarely used in the nursing community to date

Sentiment Analysis for Words and Fiction Characters From The Perspective of Computational (Neuro-)Poetics

Two computational studies provide different sentiment analyses for text segments (e.g., ‘fearful’ passages) and figures (e.g., ‘Voldemort’) from the Harry Potter books (Rowling, 1997 - 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the > 2 million words of the vector space model. After testing the tool’s accuracy with empirical data from a neurocognitive study, it was applied to compute emotional figure profiles and personality figure profiles (inspired by the so-called ‚big five’ personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into ‘good’ vs. ‘bad’ ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures

Promises and Perils of Inferring Personality on GitHub

Personality plays a pivotal role in our understanding of human actions and behavior. Today, the applications of personality are widespread, built on the solutions from psychology to infer personality. In software engineering, for instance, one widely used solution to infer personality uses textual communication data. As studies on personality in software engineering continue to grow, it is imperative to understand the performance of these solutions. This paper compares the inferential ability of three widely studied text-based personality tests against each other and the ground truth on GitHub. We explore the challenges and potential solutions to improve the inferential ability of personality tests. Our study shows that solutions for inferring personality are far from being perfect. Software engineering communications data can infer individual developer personality with an average error rate of 41%. In the best case, the error rate can be reduced up to 36% by following our recommendations

Data-driven Prediction of Internal Turbulences in Production Using Synthetic Data

Production planning and control are characterized by unplanned events or so-called turbulences. Turbulences can be external, originating outside the company (e.g., delayed delivery by a supplier), or internal, originating within the company (e.g., failures of production and intralogistics resources). Turbulences can have far-reaching consequences for companies and their customers, such as delivery delays due to process delays. For target-optimized handling of turbulences in production, forecasting methods incorporating process data in combination with the use of existing flexibility corridors of flexible production systems offer great potential. Probabilistic, data-driven forecasting methods allow determining the corresponding probabilities of potential turbulences. However, a parallel application of different forecasting methods is required to identify an appropriate one for the specific application. This requires a large database, which often is unavailable and, therefore, must be created first. A simulation-based approach to generate synthetic data is used and validated to create the necessary database of input parameters for the prediction of internal turbulences. To this end, a minimal system for conducting simulation experiments on turbulence scenarios was developed and implemented. A multi-method simulation of the minimal system synthetically generates the required process data, using agent-based modeling for the autonomously controlled system elements and event-based modeling for the stochastic turbulence events. Based on this generated synthetic data and the variation of the input parameters in the forecast, a comparative study of data-driven probabilistic forecasting methods was conducted using a data analytics tool. Forecasting methods of different types (including regression, Bayesian models, nonlinear models, decision trees, ensemble, deep learning) were analyzed in terms of prediction quality, standard deviation, and computation time. This resulted in the identification of appropriate forecasting methods, and required input parameters for the considered turbulences

A CONTENT ANALYSIS OF CONTENT ANALYSES IN IS RESEARCH: PURPOSES, DATA SOURCES, AND METHODOLOGICAL CHARACTERISTICS

Qualitative research relies primarily on qualitative data in form of texts. The method of content analysis (CA) represents a scientifically well-founded and effective solution for making valid inferences from varied sources of textual information. This paper investigates the use of CA in the context of high-quality IS research with a focus on the underlying research questions, data sources, and methodological characteristics. Building on that, distinctive patterns, trends through time, and potentials will be discussed and compared with a reference discipline (organizational research). The results indicate that the general application of CA in high-quality IS research has steadily increased. In this context, CA is used in a very wide range of ways to understand and explain complex phenomena. Furthermore, CA is frequently used to categorize primary data collections, derived, for example, from transcribed interviews or open-ended surveys, and to transform qualitative data into quantitative outcomes. Besides other methodological issues, the increasing application of computational approaches seems to distinguish the IS discipline. Finally, this paper should also serve as an introduction to CA, bring transparency with respect to the methodological characteristics, and inspire researchers to carry out further CAs in the context of IS research

ADDRESSING THE INHERENT LIMITATIONS OF TRADITIONAL MODELLING FRAMEWORK FOR SUSTAINABLE ENERGY DEVELOPMENT IN A DEVELOPING ECONOMY

This article addresses the literatures in respect of the inherent limitations of traditional modelling techniques forsustainable energy planning in a Developing Economy (DE). DE are undergoing socio-economic changes in theirenergy settings - socio-economic policies such liberalisation, financial sourcing and climate change implication ofenergy projects. This article describes a critical review of the inherent dynamics of Sustainable Energy Development(SED) and reveals the limitations of traditional planning tools such as optimisation, econometric and generalsimulation models. It argues that traditional approaches are inadequate for SED in the DE due to its inherentweakness in guiding future policy decisions. Optimisation based models may be suited for well-defined solutions,however, the macro-energy scene at a decision support level in most DE do not lend itself to simplified modellingtechnique that are rooted on past algorithms. Econometric models has seen applications across many economies,sadly, they fail to consider the technological nature of energy supply (production) and demand (consumption) in arapidly changing DE. Further, they fail to demonstrate the path taken and erroneously assumed conditions ofequilibrium for energy planning and policy formulation. This article bridge the gaps in literatures by showcasing theinherent weaknesses of traditional planning approaches and the urgent need to seek an alternative paradigm shift forsustainable energy planning and policy formulation in the DE.Keywords: Energy, Developing Economy, Frameworks, Optimization, Modelling