Panagiotis Varelas and his co-authors Mahdi Jamshidiha, Francesco Contino and Alessandro Parente, had the opportunity to present their recent paper at ECOS2023 International Conference in Gran Canaria (Spain) under the title “The public perception in designing and implementing Energy Climate Policies. A machine learning sentiment analysis on Twitter“. 

Abstract: Energy policies are an essential part of the energy transition, and public perception toward them is a quantitative indicator to evaluate them and plan them accordingly. Critical policies have recently been ratified around the globe, from the Paris Agreement in 2015 to the European Green deal in 2019 and, more recently, the REPowerEU plan from European Commission following the War in Ukraine. Implementing such policies affects numerous aspects of the energy system, some of which could be quantitative, such as technology, electricity, and fuel costs. On the other hand, public perception is necessary, and we should discover how it can affect energy policy and planning. This paper presents a quantitative analysis of twitter’s response to the latest energy and climate policies. Our approach classifies tweets into two categories, one according to the topic within three classes: Economy, Ecology, and Society, and the second based on the sentiment: Positive, Negative, and Neutral. Many algorithms are available in the literature for this study; we implemented kNN, SVC, Naïve Bayes, logistic regression, and random forest. Based on our findings, most tweets, around 80%, are neutral, with less being classified as positive and even less as negative. Regarding the subject category, classification is equally distributed within the three topics. The results could give policymakers insight into their decisions and their acceptance by the general population. We would extend our approach to unsupervised learning in the future to compare and improve algorithm performances.