Electric Vehicles (EVs) are a rapidly growing com- ponent of the automotive industry and are projected to have 24 percentage of the overall United States light duty vehicle market by 2030(Becker et al., 2009). Furthermore, the US and other countries have bet big on Battery Electric Vehicles (BEVs), allotting funding for charging infrastructure, sub- sidies and tax credits. Correspondingly, the stock price of EV companies like Tesla have recently far exceeded those of traditional auto manufactur- ers, helping to illustrate the bullish outlook many consumers and investors have toward EVs in gen- eral. Despite this, there remain concerns among both consumers and experts about various aspects of electric cars, and despite the excitement sur- rounding them, EV adoption rates hovered around 1.8% in 2020 (Chandra et al., 1981). These can include topics from battery malfunctions, range- anxiety, concerns about lithium and mineral min- ing, charging infrastructure, and indirectly-related topics such as self-driving software. These vary- ing opinions in EVs may arise from a variety of sources, but we specifically hypothesize that a dif- ference in sentiment and topic choice can be seen between News Articles and EV consumer reviews. To help answer this hypothesis we will use a token-wise and document-wise sentiment analysis applied to each data set and will then compare our sentiment values for a statistically significant differ- ence. We will also use topic-modelling to examine what, if any, differences in topic and topic senti- ment exist between news and consumer Reviews.