Work

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

Year: 2019

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.

Source: Journal of the Association for Information Systems

Authors: Shan, Shan, Li, Honglei, Li, Yulei

Institutions: Northumbria University, Durham University

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: green

OpenAlex ID: W3012033673

FWCI: 0.2

Cites: 1

Cited by: 2

Related to: 10