Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

Abstract

Public acceptance and support for renewable energy are important determinants of the low-carbon energy transition. This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform on which people post messages, known as tweets. We filtered tweets specific to solar energy and performed a classification task using Robustly optimized Bidirectional Encoder Representations from Transformers (RoBERTa). Our RoBERTa-based sentiment classification model, fine-tuned with 6300 manually annotated tweets specific to solar energy, attains 80.2% accuracy for ternary (positive, neutral, or negative) classification. Analyzing 266,686 tweets during the period of January to December 2020, we find public sentiment varies widely across states (Coefficient of Variation =164.66%). Within the study period, the Northeast U.S. region shows more positive sentiment toward solar energy than did the South U.S. region. Public opinion on solar energy is more positive in states with a larger share of Democratic voters in the 2020 presidential election. Public sentiment toward solar energy is more positive in states with consumer-friendly net metering policies and a more mature solar market. States that wish to gain public support for solar energy might want to consider implementing consumer-friendly net metering policies and support the growth of solar businesses.

Green energy: identifying development trends in society using Twitter data mining to make strategic decisions

“…In the case of renewable energies, although they are an indisputable reality, public acceptance and support are factors relevant to both renewable energy policies and market conditions (Kim et al, 2020). Accordingly, studies have analyzed the feelings of Twitter users in relation to renewable energies in general (Jain; Jain, 2019) as well as specific types such as solar energy (Reboredo;Ugolini, 2018;Li et al, 2019;Kim et al, 2020).…”

Development and Application of a Big Data Analysis-Based Procedure to Identify Concerns about Renewable Energy

“…There have been studies to analyze texts posted on social network services (SNS) such as Twitter and Reddit. Such studies included the analysis results of the regional perception of renewable energy [12], the regional perception of solar energy [13], and the difference in perception between the two countries on climate change [14]. These studies identified the emotional expressions SNS users wanted to share through SNS, but could not figure out what they were specifically curious about.…”

“…Li et al collected tweets on Twitter about fossil fuels and renewable energy, analyzed them using the Valence Aware Dictionary and sEntiment Reasoner tool to understand public perception, and compared the analysis results for the three different regions [12]. Kim et al collected tweets about solar energy generation in the United States, conducted sentiment analysis using the robustly optimized bidirectional encoder representations from transformers pretraining approach sentiment classification model, and compared them with the states' renewable energy policies [13]. Loureiro et al collected tweets about climate change in the UK and Spain and used the National Research Center Canada Emotion Lexicon sentiment dictionary to evaluate public preferences regarding the various energy policies [14].…”

Evaluating and improving social awareness of energy communities through semantic network analysis of online news

“…Semantic network analysis proves to be a promising approach for analyzing online news [43], providing information from the study of the co-occurrence of concepts and their semantic associations through automatic coding methodologies [44]. It can reveal essential relationships among concepts in the energy domain.…”

“…With respect to the existing studies, we use a new approach to analyze news and its semantic networks, e.g. [40,43,44]. To the best of our knowledge, we are the first to use this specific approach to study the media attention on the topic of energy communities.…”