This study analyzes sentiment analysis techniques applied to patient comments on social media platforms, specifically focusing on discussions about treatments for depression. Depression, a serious and widespread mental illness, is exacerbated by the psychological pressures of the modern lifestyle. Various treatments, including drugs and therapies, are used to improve the quality of life of depressed patients. Today, social networks and online platforms allow patients to share their experiences and feelings about these treatments, generating an exponential amount of unstructured and semi-structured data. To assess the sentiments expressed in patient comments, we used a meticulously labelled dataset from various online platforms. The study compared the performance of three machine learning algorithms: Support Vector Machine (SVM), Random Forest and Decision Tree and two algorithms of Deep Learning: LSTM and CNN in order to create a platform that enables healthcare professionals to accurately predict the sentiments of patients regarding their treatments and medications, thereby enhancing the overall treatment experience and outcomes for patients with depression.