A Sentiment Analysis of Peer to Peer Energy Trading Topics from Twitter

Authors

Shan Shan, Northumbria University, Newcastle Upon Tyne, UKFollow
Honglei Li, Northumbria University, UKFollow
Yulei Li, Durham University, Durham, UKFollow

Corresponding Author

Shan Shan*, Northumbria University, Newcastle Upon Tyne, UK, shan.shan@northumbria.ac.uk

Document Type

Article

Abstract

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.

Recommended Citation

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.
https://aisel.aisnet.org/iceb2019/26