In this work we explore the use of Twitter feeds and news articles as alternative data sources to predict the closing stock prices of leading green companies such as First Solar, Siemens, Plug Power and others. We show our methods to acquire data from alternative sources such as Twitter and news articles and perform sentiment analysis using NLTK over time. Using the time series obtained, we perform lag correlation and Granger causality, and TF-IDF analysis to investigate whether information gained from the alternative data sources and the stock market prices of leading green energy companies are in any way correlated.