论文标题
公众对太阳能的情感：使用基于变压器的语言模型的Twitter的意见挖掘
Public Sentiment Toward Solar Energy: Opinion Mining of Twitter Using a Transformer-Based Language Model
论文作者
论文摘要
公众接受和对可再生能源的支持是可再生能源政策和市场状况的重要决定因素。本文使用来自Twitter的数据来研究美国对太阳能的情感，Twitter是一个微博平台，人们在该平台中发布消息，称为推文。我们过滤了特定于太阳能的推文，并使用来自变形金刚（Roberta）的强大优化双向编码器表示进行了分类任务。在1月下旬至2020年7月初，分析71,262条推文，我们发现在各州之间，公众情绪差异很大。在研究期内，美国东北部对太阳能表现出比美国南部地区更积极的情绪。太阳辐射与各州太阳情绪的变化无关。我们还发现，公众对太阳能的情绪与可再生能源政策和市场状况相关，特别是可再生投资组合标准（RPS）目标，客户友好的净计量政策以及成熟的太阳能市场。
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.
