The Power of Language: Understanding Sentiment Towards the Climate Emergency using Twitter Data
This work addresses the problem of linking public sentiment to economic indicators for climate policy, but it is incremental with inconclusive results on attitude variation.
The study tackled the relationship between sentiment towards the Climate Emergency and Crude Oil Futures using Twitter data, achieving a root mean squared error of 0.196 and 0.209 on training and testing data for forecasting.
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).