NFT Bidding Behaviours and Sentiment Analysis

Published: 15 December 2023| Version 2 | DOI: 10.17632/sydxb9rwyb.2
Darren Shannon


This folder serves as a repository for hosting the raw data and jupyter notebook files needed to recreate the analysis for 'Dutch auction dynamics in non-fungible token (NFT) markets'. Purpose of File 'Telegram_Sentiment_Analysis_using_VADER.ipynb': 1. Deconstructing and cleaning 33,533 Telegram Forum messages to extract further insights on the bidding and listing behaviours of investors involved in a fledgling NFT marketplace 2. Applying a Sentiment Analysis model specialising in social media communications (VADER) 3. Extracting sentiment scores, for later analysis against NFT listing and sales data 4. Visually inspecting the evolution of forum participation and investor sentiment over an 8-week study period. Purpose of File 'NFT_Plots.ipynb': 1. Visually inspecting the evolution of 29,154 NFT listings and 5,011 NFT sales data over an 8-week period. The data required to run both notebooks are available in the 'NFT_Dutch_Auction_Study_Data.xlsx' file Jupyter Notebook(s) Author: Dr. Darren Shannon. All errors my own. Journal Article Authors: Dr. Darren Shannon, Prof. Michael Dowling, Dr. Marjan Zhaf, Dr. Barry Sheehan More information on running the files: - Virtual environment / project generation / library / root files are not provided and will need to be created before running the Python code. - Minor code amendments are required in each notebook to open the data file and save generated images locally. - Information on the library packages required to run the analysis are provided in the relevant notebooks. - Please direct any detected errors in the code / data to:



University of Limerick


Economics, Finance, Behavioral Finance, Alternative Investment, Online Auction, Behavioral Economics, Digital Economy, Sentiment Analysis, Cryptocurrency