Abstract: The rise of digital platforms has led to a massive influx of textual data. While traditional textual analysis techniques have been effective, analyzing large datasets is becoming impractical due to the required time and resources. To demonstrate the usefulness of text mining as an alternative, this study analyzed data extracted from an emergency remote learning (ERL) environment. Free-form responses from a series of cross-sectional surveys (2020–2022) were analyzed using word frequency, collocation, concordance, topic modeling, and sentiment analyses. According to the findings, the most commonly occurring unigram and bigram in the text corpus were “hard” and “mental health,” respectively. Three primary themes based on lived experiences were identified, namely individual, academic, and technological challenges, and another three themes emerged from coping strategies, including entertainment, relationship, and health-related mechanisms. Negative sentiment toward the ERL setup was also evident in the text corpus. Overall, the combination of text mining techniques allowed for a comprehensive exploration of the linguistic features of the corpus and provided a multifaceted understanding of the selected phenomenon. Consequently, this study endorses text mining as a methodology for analyzing large volumes of textual data.