A sentiment analysis of peer to peer energy trading topics from twitter

Shan Shan*, Honglei Li, Yulei Li

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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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.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalProceedings of the International Conference on Electronic Business (ICEB)
Publication statusPublished - 31 Dec 2019
Event19th International Conference on Electronic Business, ICEB 2019 - Newcastle upon Tyne, United Kingdom
Duration: 8 Dec 201912 Dec 2019

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