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

Shan, Shan, Li, Honglei and Li, Yulei (2019) A sentiment analysis of peer to peer energy trading topics from twitter. Proceedings of the International Conference on Electronic Business (ICEB), 2019. pp. 1-12. ISSN 1683-0040

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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.

Item Type: Article
Uncontrolled Keywords: Peer to Peer energy trading, data mining, hLDA, feature engineering
Subjects: G400 Computer Science
N100 Business studies
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: John Coen
Date Deposited: 24 Jun 2020 13:07
Last Modified: 25 Jun 2020 14:00
URI: http://nrl.northumbria.ac.uk/id/eprint/43569

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