Empowerment of women's entrepreneurship in family business through Twitter

Abstract

Purpose The main purpose is to provide a global understanding of the role of women in entrepreneurship and family businesses, enabling the evaluation of the impact and the sentiment their role generates. To this end, empowerment and businesswomen's positioning through user-generated content (UCG) on Twitter is assessed. Design/methodology/approach The research is carried out from a quantitative and qualitative perspective through the evaluation of UGC from the social platform Twitter. A total of 37,852 tweets have been collected and subsequently analysed about the role of entrepreneurial women. For that purpose, a set of supervised machine learning algorithms have been developed for sentiment analysis, as a natural language processing (NLP) technique, outlining random forest as the one with the highest accuracy. Finally, social network analysis (SNA) techniques and graph theory are applied to a generated text-to-network, which enables the identification of the most relevant topics in the discussion. Findings The results revealed a positive relationship in the sentiment of the generated content in relation to women entrepreneurs and leaders. An increasing trend was evidenced in the number of published tweets, as well as in the identified topics, highlighting the needs and challenges faced by women in the business environment as the most widely discussed. Research limitations/implications The study develops both theoretical and practical implications so that the findings result in applications in academia and society. The performed analysis creates consciousness about the challenges of women in society, specifically in entrepreneurship. Originality/value The study contributes to further enriching the literature on women's entrepreneurship by addressing UGC via Twitter around the role of women, entrepreneurship and power positions.