| Summary: | The COVID-19 pandemic has had a significant impact on mental health, resulting in anxiety and other issues among many individuals due to the lockdowns implemented to curb its spread. With the world moving toward 2030, it is crucial to reduce premature mortality from non-communicable diseases through prevention and treatment. Sustainable Development Goal (SDG) 3 emphasizes prioritizing mental health and well-being to address the increasing burden of mental health issues. The study utilized text clustering through the K-Means algorithm to gain a better understanding of the mental health issues people are facing. The Term Frequency-Inverse Document Frequency (TF-IDF) was used to determine each word's weight after extracting tweets from Twitter and preprocessing the data. The K-Means clustering algorithm was then employed in the data, which revealed that the clusters could be classified into three categories of mental health: ‘stress,’ ‘depression,’ and ‘pressure.’ It was found that using three clusters provided more dependable outcomes since clusters with more than three tended to have overlapping mental health conditions. This study sheds light on the mental health problems that people face during the COVID-19 pandemic, which can help guide efforts to support those in need. Moreover, it would be more beneficial to incorporate Bahasa Malaysia in future research since there has yet to be much exploration done on this language despite it being Malaysia's official language. By adopting a holistic approach and prioritizing mental health, we can work toward ensuring a healthier and happier future for everyone. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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