Working in Detail: How LSTM Hyperparameter Selection Influences Sentiment Analysis Results

Sentiment analysis of written customer reviews is a powerful way to generate knowledge about customer attitudes for future marketing activities. Meanwhile, Deep Learning as the most powerful machine learning method is of particular importance for sentiment analysis tasks. Due to this current relevance, an LSTM network based on a literature review to solve the challenging classification task of the IMDB LargeMovie Dataset is created. Hyperparameters are varied separately from each other to better understand their single influences on the overall model accuracy. Furthermore, we transformed variants with positive impacts into a final model in order to investigate whether the impacts can be cumulated. While preparing the amount of training data and the number of iteration steps resulted in a higher accuracy, pre-trained word vectors and higher network capacity did not work well separately. Even though implementing the variants with positive influences together raised the model´s performance, the improvement was lower than some single variants

Examining Technostress at Different Types of Data Scientists’ Workplaces

Data scientists represent a heterogeneous occupational group that has reached high relevance due to the wide-spread availability of quantitative data generated in the rapid progress of digital transformation. These employees play a crucial role in gaining competitive advantages for companies out of such big data. In this context, employees who frequently analyse data often occupy different job titles and, therefore, are difficult to detect. At the same time, a psychological downside of digitalization, which is called technostress, has risen. However, these issues caused by the use of information and communication technologies are rarely examined in the context of specific occupational groups and workplace attributes. Considering these challenges, this article extends current technostress research by focusing on technostress within the specific job class of data scientists. We classify different types of data scientists’ workplaces through performing latent class analysis using several workplace attributes within a sample of n=486 German data scientists. Subsequently, we reveal considerable distinctions between these classes regarding the intensity of technostress creators, strains due to ICT use, and job performance. We discuss our empirical findings and deliver theoretical contributions as well as practical implications for both employees and employers and starting points for future research

Belastungsfaktoren der digitalen Arbeit: Eine beispielhafte Darstellung der Faktoren, die digitalen Stress hervorrufen

Die schnell voranschreitende Durchdringung des Arbeitslebens mit digitalen Technologien und Medien bringt viele Chancen, aber auch substanzielle Risiken und Nachteile mit sich. Es treten umfangreiche Änderungen im Belastungs- und Beanspruchungsprofil von Erwerbstätigen auf. Die potenzielle Folge: digitaler Stress. Die Studie „Gesund digital arbeiten?!“ des Forschungsprojekts „PräDiTec – Prävention für sicheres und gesundes Arbeiten mit digitalen Technologien“, gefördert durch das Bundesministerium für Bildung und Forschung (BMBF) im Programm „Innovation für die Produktion, Dienstleistung und Arbeit von morgen“ (Fördernummer O2L16D030), untersucht mittels einer groß angelegten Befragung von über 5.000 Erwerbstätigen die Verbreitung von digitalem Stress, dessen Einflussfaktoren und dessen Folgen in Deutschland. In der Studie konnten bei der Arbeit mit digitalen Medien und Technologien mindestens zwölf verschiedene Belastungsfaktoren identifiziert werden. Diese Broschüre soll ein tieferes Verständnis über diese Belastungsfaktoren geben. Jeder Belastungsfaktor wird definiert und mit einem Beispielsszenario veranschaulicht. Anschließend werden Lösungsvorschläge aufgezeigt