Desolation, characterized by profound feelings of loneliness, has emerged as a significant psychological concern with far-reaching impacts on public health, leading to various ailments, mental health disorders, and even mortality. This growing trend of desolation among individuals calls for immediate attention and preventive measures. In this study, we propose a data-driven approach to mine desolation-related sentiments expressed on social media platforms, specifically Twitter, where users frequently share their thoughts and emotions in posts. These posts, often reflecting positive and negative feelings, are valuable for analyzing public sentiment. Our approach targets collecting and analyzing posts expressing desolation, employing data mining techniques to identify and quantify such expressions over time. Utilizing Twitter’s public API, we gather data from tweets to detect patterns in desolation-related language, capturing the frequency and intensity of these sentiments at specific timestamps. This temporal aspect allows us to track fluctuations in desolation levels, observing whether sentiments of loneliness increase or decrease during specific periods. By identifying tweets with keywords and phrases associated with desolation, our model can classify and analyze content effectively, producing insights into the prevalence and trends of desolation over time. The results are then visualized, plotting periods when desolation sentiments peak, thus highlighting critical moments for potential intervention.