The emergence of the Peer-to-Peer (P2P) energy trading platforms provides a new method for the general public to use and trade green energy. How to design the peer to peer energy trading platform thus becomes important in facilitating user trading experience. This study will use the data mining method to evaluate factors impacting P2P energy trading experience. Python was used to analyze data extracted from Twitter and Natural Language Processing (NLP) method was implemented with hierarchical Latent Dirichlet Process (hLDA) model. The study’s findings will be examined in detail.
Shan, Shan; Li, Honglei; and Li, Yulei, "A Sentiment Analysis of Peer to Peer Energy Trading Topics from Twitter" (2019). ICEB 2019 Proceedings (Newcastle Upon Tyne, UK). 26.