Moodley, AvashlinNaidoo, Privolin2026-03-202026-03-202025-113006-6492DOI: https://doi.org/10.55492/dhasa.v5i02.6723http://hdl.handle.net/10204/14775In times of collective discomfort and dissat isfaction, people often find solace in shared adversity on social media platforms like X (for merly known as Twitter). These platforms offer a unique window into the public’s emotions and viewpoints concerning common challenges. In 2022, South Africa experienced an electricity crisis, during which the country was subjected to rolling blackouts, commonly known as load shedding, by Eskom, the country’s primary electricity provider, to prevent a national elec tricity grid shutdown. This study conducted a data-driven exploration of the public discourse surrounding Eskom and loadshedding on X us ing natural language processing and data sci ence techniques. The dataset utilised for this study comprised tweets containing keywords related to Eskom and loadshedding. The study delved into the topics of discussion by apply ing topic modelling techniques to uncover la tent themes within the discourse. The topics were analysed through a multifaceted lens to unpack and highlight patterns within the sen timents, emotions and biases that underpin conversations related to loadshedding and Es kom. A notable inclusion in the analysis was the incorporation of sarcasm classifications, which enhanced the interpretation of the emo tion and sentiment within the topics discussed. The findings uncovered from the analysis were contrasted with loadshedding-related events in 2022 to understand the public discourse as the electricity crisis escalated. The methodology of this study provides a framework for utilis ing natural language processing techniques to uncover and examine the perspectives of a col lective within discourse related to events of shared interest.FulltextenSocial mediaText miningNatural lan guage processingTopic modellingLoadsheddingEskomConference PresentationN/A