This study presents an approach to Aspect-Based Sentiment Analysis (ABSA) using Natural Language Processing (NLP) techniques to explore public sentiment across 12 suburban neighborhoods in Finland. We employed and compared a range of machine learning models for sentiment classification, with the RoBERTa model emerging as the best performer. Using RoBERTa, we conducted a comprehensive sentiment analysis(SA) on a manually annotated dataset and a predicted dataset comprising 32,183 data points to investigate sentiment trends over time in these areas. The results provide insights into fluctuations in public sentiment, highlighting both the robustness of the RoBERTa model and significant shifts in sentiment for specific neighborhoods over time. This research contributes to a deeper understanding of neighborhood sentiment dynamics in Finland, with potential implications for social research and urban development.