TY - JOUR
T1 - Residents' sentiments towards electricity price policy
T2 - Evidence from text mining in social media
AU - Sun, Yefei
AU - Wang, Zhaohua
AU - Zhang, Bin
AU - Zhao, Wenhui
AU - Xu, Fengxin
AU - Liu, Jie
AU - Wang, Bo
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/9
Y1 - 2020/9
N2 - According to the theory of sentiment motivation, residents' sentiments are an important factor affecting residents' electricity consumption behavior. Based on 149.95 thousand microblog posts and natural language processing methods, we analyze the time-varying characteristic, seasonal characteristic and mechanism of residents' sentiments towards electricity pricy policy. The main results are as follows. (1) Although the step tariff policy leads to the rise of electricity price, residents show positive sentiments to electricity price policy. (2) The intensity of residents’ sentiments is characterized by three stages. In the early stage, residents' sentiments towards electricity pricy policy are the most negative. In the middle stage, residents’ negative sentiments towards policy gradually decreases. In the later stage, residents show positive sentiments as a whole. (3) Residents' sentiments towards policy show obvious seasonal difference. Residents' negative sentiments show the highest intensity and obvious convergence characteristic in summer, mainly around 0, while residents' sentiments towards policy diverge in a positive direction in winter. (4) Residents' negative sentiments towards electricity policy result from smart meters, electric heating, renewable energy development and electric sector. The driving forces of residents' positive sentiments towards policy include policy cognition, public participation and policy content. Social media data provides real-time feedback on policy, which is of great significance to the formulation and improvement of policy.
AB - According to the theory of sentiment motivation, residents' sentiments are an important factor affecting residents' electricity consumption behavior. Based on 149.95 thousand microblog posts and natural language processing methods, we analyze the time-varying characteristic, seasonal characteristic and mechanism of residents' sentiments towards electricity pricy policy. The main results are as follows. (1) Although the step tariff policy leads to the rise of electricity price, residents show positive sentiments to electricity price policy. (2) The intensity of residents’ sentiments is characterized by three stages. In the early stage, residents' sentiments towards electricity pricy policy are the most negative. In the middle stage, residents’ negative sentiments towards policy gradually decreases. In the later stage, residents show positive sentiments as a whole. (3) Residents' sentiments towards policy show obvious seasonal difference. Residents' negative sentiments show the highest intensity and obvious convergence characteristic in summer, mainly around 0, while residents' sentiments towards policy diverge in a positive direction in winter. (4) Residents' negative sentiments towards electricity policy result from smart meters, electric heating, renewable energy development and electric sector. The driving forces of residents' positive sentiments towards policy include policy cognition, public participation and policy content. Social media data provides real-time feedback on policy, which is of great significance to the formulation and improvement of policy.
KW - Electricity price policy
KW - Residents’ sentiments
KW - Sentiment mechanism
KW - Text data mining
UR - http://www.scopus.com/inward/record.url?scp=85084976815&partnerID=8YFLogxK
U2 - 10.1016/j.resconrec.2020.104903
DO - 10.1016/j.resconrec.2020.104903
M3 - Article
AN - SCOPUS:85084976815
SN - 0921-3449
VL - 160
JO - Resources, Conservation and Recycling
JF - Resources, Conservation and Recycling
M1 - 104903
ER -
