Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a
  Transformer-Based Language Model   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.

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The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph