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Female Entrepreneurship has gained interest over the last 20 years. Therefore, this paper analyses 7,320 articles of the research field ‘women in entrepreneurial context’ published in 885 journals. The sample is analyzed by using a machine learning and text mining based methodological approach. Aiming to provide a broad overview over the research literature, 41 clusters and 11 superordinate topics were identified. Major developments of research attention are outlined by analyzing bibliometric data of the period from 2000 to 2020. Overall growth in terms of research attention measured by the development of yearly citations per article is best noticeable in clusters ‘corporate social responsibility’, ‘brand’, and ‘corporate (-governance)’, and in superordinate topics ‘performance’, ‘education’, and ‘corporate (board/ management)’. There are also indicators for an overall increase of research attention and cluster variety. The synthesis provides an insight into most trending superordinate topics. Therefore, this literature review gives a comprehensive and descriptive overview as well as an insight into thematic trend developments of the research field.
Generating synthetic data is a relevant point in the machine learning community. As accessible data is limited, the generation of synthetic data is a significant point in protecting patients' privacy and having more possibilities to train a model for classification or other machine learning tasks. In this work, some generative adversarial networks (GAN) variants are discussed, and an overview is given of how generative adversarial networks can be used for data generation in different fields. In addition, some common problems of the GANs and possibilities to avoid them are shown. Different evaluation methods of the generated data are also described.