Energy prices have gone up gradually since last year, but a drastic hike has been observedrecently in the past couple of months, affecting people’s thrift. This, coupled with the load shedding andenergy shortages in some parts of the world, led many to show anger and bitterness on the streets and on socialmedia. 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 onsocial media posts on this topic. Therefore, in this study, we propose a solution that takes advantage of botha transformer-based sentiment analysis method and topic modeling to explore public engagement on Twitterregarding energy prices rising. The former method is employed to annotate the valence of the collected tweetsas positive, neutral and negative, whereas the latter is used to discover hidden topics/themes related to energyprices for which people have expressed positive or negative sentiments. The proposed solution is tested ona dataset composed of 366,031 tweets collected from 01 January 2021 to 18 June 2022. The findings showthat 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, in2022 when negative sentiment was dominant.