<![CDATA[The mental health of Generation Z employees has become an important issue in the modern workplace due to increasing psychological pressure. Social media X (formerly Twitter) is often used as an anonymous space to express stress, complaints, and emotional conditions openly. This study aims to develop an early detection system for depression risk among Generation Z employees using Natural Language Processing (NLP) and Deep Learning approaches. The proposed method integrates a 1D-Convolutional Neural Network (1D-CNN) model with Word2Vec word representations to capture linguistic patterns that indicate emotional states. The dataset consists of text from social media X, processed through preprocessing stages such as cleaning, normalization, tokenization, and stemming to improve data quality and analysis accuracy. The model is then trained and tested to classify depression risk levels. Performance evaluation is conducted using accuracy, precision, and recall metrics to assess the model’s effectiveness. The results are expected to contribute to the development of early mental health detection systems for companies and healthcare professionals, serving as a preventive tool to maintain the psychological well-being of Generation Z employees in increasingly complex digital work environments.]]>
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