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

Shan Shan, Honglei Li , Yulei Li

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

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
Title of host publicationProceedings of The 19th International Conference on Electronic Business
EditorsEldon Y. Li, Honglei Li
PublisherICEB
Pages1-12
Number of pages12
Publication statusPublished - Dec 2019
Externally publishedYes
EventThe 19th International Conference on Electronic Business - Hilton, Newcastle upon Tyne, United Kingdom
Duration: 8 Dec 201912 Dec 2019

Publication series

Name
ISSN (Electronic)1683-0040

Conference

ConferenceThe 19th International Conference on Electronic Business
Country/TerritoryUnited Kingdom
CityNewcastle upon Tyne
Period8/12/1912/12/19

Keywords

  • Peer to Peer energy trading
  • data mining
  • hLDA
  • feature engineering

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