With the widespread growth of various social network tools and platforms, analyzing and understanding societal response and crowd reaction to important and emerging social issues and events through social media data is increasingly an important problem. However, there are numerous challenges towards realizing this goal eāffectively and eĀciently, due to the unstructured and noisy nature of social media data. Će large volume of the underlying data also presents a fundamental challenge. Furthermore, in many application scenarios, it is oČen interesting, and in some cases critical, to discover paäerns and trends based on geographical and/or temporal partitions, and keep track of how they will change overtime. Ćis brings up the interesting problem of spatio-temporal sentiment analysis from large-scale social media data. Ćis paper investigates this problem through a data science project called %u201CUS Election 2016, What Twiāer Says%u201D. Će objective is to discover sentiment on Twiäer towards either the democratic or the republican party at US county and state levels over any arbitrary temporal intervals, using a large collection of geotagged tweets from a period of 6 months leading up to the US Presidential Election in 2016. Our results demonstrate that by integrating and developing a combination of machine learning and data management techniques, it is possible to do this at scale with effective outcomes. Će results of our project have the potential to be adapted towards solving and inÉfluencing other interesting social issues such as building neighborhood happiness and health indicators.