  Understanding how attitudes towards the Climate Emergency vary can hold the
key to driving policy changes for effective action to mitigate climate related
risk. The Oil and Gas industry account for a significant proportion of global
emissions and so it could be speculated that there is a relationship between
Crude Oil Futures and sentiment towards the Climate Emergency. Using Latent
Dirichlet Allocation for Topic Modelling on a bespoke Twitter dataset, this
study shows that it is possible to split the conversation surrounding the
Climate Emergency into 3 distinct topics. Forecasting Crude Oil Futures using
Seasonal AutoRegressive Integrated Moving Average Modelling gives promising
results with a root mean squared error of 0.196 and 0.209 on the training and
testing data respectively. Understanding variation in attitudes towards climate
emergency provides inconclusive results which could be improved using
spatial-temporal analysis methods such as Density Based Clustering (DBSCAN).
