Marketing challenges in the #MeToo era: gaining business insights using an exploratory sentiment analysis

Abstract

The #MeToo movement is among the most impressive social movements of recent years that have attracted stakeholders’ attention and changed social mindsets. The present study seeks to provide a deeper understanding of the challenges involved in the #MeToo movement by identifying the main issues regarding business and marketing activities. To this end, the analysis of user-generated content (UGC) on Twitter was performed to extract the tweets with the hashtag “#MeToo” (31,305 tweets). Then, a Latent Dirichlet Allocation (LDA) model was applied to this database to identify topics. In the next step, using a Supervised Vector Machine (SVM) type analysis, we classified the tweets according to the sentiment they express (positive, negative, and neutral). Finally, we performed data text mining using the NVivo software. Our findings underscore the importance of (i) gender equality in communication campaigns, (ii) gender equality at work and (iii) social mobilizations in social networks, as well as suggest that (iv) marketing advertisers should become more inclusive and respectful in their advertising and marketing campaigns. The identified topics may be a starting point for future research on social movements, sociology, sexuality, or machismo in work environment, business and marketing strategies.

Abstract

Computer science; #MeToo; Sexual harassment; Data text mining; Sentiment analysis; Social media content; Twitter.