Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers

Abstract

Energy prices have gone up gradually since last year, but a drastic hike has been observed recently in the past couple of months, affecting people's thrift. This, coupled with the load shedding and energy shortages in some parts of the world, led many to show anger and bitterness on the streets and on social media. Despite subsidies offered by many Governments to their citizens to compensate for high energy bills, the energy price hike is a trending topic on Twitter. However, not much attention is paid to opinion mining on social media posts on this topic. Therefore, in this study, we propose a solution that takes advantage of both a transformer-based sentiment analysis method and topic modeling to explore public engagement on Twitter regarding energy prices rising. The former method is employed to annotate the valence of the collected tweets as positive, neutral and negative, whereas the latter is used to discover hidden topics/themes related to energy prices for which people have expressed positive or negative sentiments. The proposed solution is tested on a dataset composed of 366,031 tweets collected from 01 January 2021 to 18 June 2022. The findings show that people have discussed a variety of topics which directly or indirectly affect energy prices. Moreover, the findings reveal that the public sentiment towards these topics has changed over time, in particular, in 2022 when negative sentiment was dominant.

Evaluation of adjective and adverb types for effective Twitter sentiment classification

“…Kastrati et al [ 48 ] learnt the public engagement on rising energy prices using sentiment analysis. Author experimented the dataset of tweets collected between January 2021 to June 2022.…”

What motivates and demotivates energy savings in times of crisis? – An argument mining analysis using X/Twitter data

“…While social media data has been increasingly used to study discourses in different elds of research, its application in the eld of energy research is rather limited. A few studies have used the approach for energy-related issues (Müller-Hansen et al, 2022;Ringel et al, 2021), mainly focusing on perceptions towards renewable energy (Abdar et al, 2020;Kim et al, 2021;Li et al, 2019;Loureiro & Alló, 2020) and nuclear energy (Arlt et al, 2019;Khatua et al, 2020), with limited attention to energy crises (Kastrati et al, 2023). By contrast, a large body of research uses social media data to study opinions on medical and health issues (Dunn et al, 2015 (Gadenne et al, 2011;Poortinga et al, 2004;Steg, 2008), the role of information (Abrahamse et al, 2007;Delmas et al, 2013), the role of policy (Abrahamse et al, 2005;Reuter et al, 2021) and through the lens of behavioral economics (Frederiks et al, 2015).…”