<![CDATA[This study aims to analyze the dynamics of public opinion related to quarter life crisis on platform X through a sentiment analysis approach based on machine learning Support Vector Machine (SVM) algorithm is used to classify positive and negative sentiments from text data. A total of 6.312 tweets were collected with the keyword “quarter life crisis” from January 2024 to January 2025. The data was then processed through the stages of text cleaning, tokenization, stopword removal, stemming, and lexicon-based sentiment labeling. The classification process is carried out using SVM with a data division of 80% training and 20% test. The results showed an accuracy of 81.57% with a sentiment distribution of 59.3% negative and 40.7% positive. Implementation was done on Google Colab platform with evaluation using confusion matrix and classification report. The fingdings prove the effectiveness of SVM in analyzing psychosocial phenomena on social media and provide an empirical basis for the development of digital data-based mental health interventions. The machine learning pipeline optimized in this study can be used as a reference for other studies in analyzing psychological phenomena on social media]]>
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