Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language Model

TL;DR

This study analyzes Twitter data to assess U.S. public sentiment toward solar energy, revealing regional differences and correlations with policies and market maturity using transformer-based language models.

Contribution

It introduces a novel application of RoBERTa for classifying solar energy sentiment on Twitter and links sentiment variations to policy and market factors.

Findings

Northeastern U.S. shows more positive sentiment than the South.

Public sentiment correlates with RPS targets, net metering policies, and market maturity.

Solar radiation does not influence sentiment variation across states.

Abstract

Public acceptance and support for renewable energy are important determinants of renewable energy policies and market conditions. This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform in 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). Analyzing 71,262 tweets during the period of late January to early July 2020, we find public sentiment varies significantly across states. Within the study period, the Northeastern U.S. region shows more positive sentiment toward solar energy than did the Southern U.S. region. Solar radiation does not correlate to variation in solar sentiment across states. We also find that public sentiment toward solar correlates to renewable energy policy and market conditions, specifically, Renewable Portfolio Standards (RPS) targets, customer-friendly net metering policies, and a mature solar market.