<![CDATA[Recent advances in digital technology have made it possible to access and analyze large amounts of data, providing scholars in psychology with new ways to gain insights from qualitative sources such as social media, news articles, and interviews. Traditional methods, however, struggle to handle such large data volumes effectively. Text mining offers an efficient solution, using algorithms to find patterns and themes in large text datasets. This article introduces text mining techniques for analyzing large-scale qualitative data, using two issues of Jurnal Psikologi Ulayat (JPU) as a case study. Using techniques such as word frequency analysis, word clouds, bi-grams, and pairwise correlations, five main themes were identified: (1) Indonesian-based psychology, (2) mental health, (3) interpersonal and family relationships, (4) psychometric assessments, and (5) social phenomena related to fandom and pop culture. This study demonstrates the potential of text mining as an efficient approach to uncover patterns and themes in large qualitative datasets.]]>
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