Abstract: The debate on climate change has increasingly attracted attention, especially among young people, since the foundation of the movement Friday for Future and the raising fame of Greta Thunberg. Social media websites can be used as a data source for mining public opinion on a variety of subjects including climate change. Twitter, in particular, allows for the evaluation of public opinion across time. Although it is a known problem that Twitter population is biased with respect to the whole population, it is also true that Twitter users are more likely to be young people. For this reason, the sentiment analysis of Twitter textual data on climate topics provides valuable insights into the climate discussion and could be considered as representative of the rising climate movement. In this study, a large dataset of Italian tweets between 2016 and 2022 containing a set of keywords related to climate change (e.g. Global warming, sustainable development, etc.) is analysed using volume analysis and text mining techniques such as topic modelling and sentiment analysis. Topic modelling, performed using word embedding, allows validating the keywords’ set and providing the prevalent discussion in Italy about the climate agenda and the major concerns related to climate emergency. Both daily volume and sentiment of tweets series have been analysed. The first series allows assessing the Italian participation to the climate debate, while the latter provides useful insights on the overall evolving mood during these years. In particular, we show that the major Italian concerns are related with global warming with a negative mood while a positive mood is recorded when public policies on environment are implemented.
Keywords: Sentiment analysis, climate change, text mining, Twitter, word embeddings
