Ibrahim, A. 2021. Analyzing BTC’s Trend During COVID-19 Using A Sentiment Consensus Clustering (SCC). 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Vancouver, Canada 27 - 30 Oct 2021 IEEE . https://doi.org/10.1109/IEMCON53756.2021.9623182
| Title | Analyzing BTC’s Trend During COVID-19 Using A Sentiment Consensus Clustering (SCC) |
|---|---|
| Authors | Ibrahim, A. |
| Type | Conference paper |
| Abstract | Tweets from social media can help in providing an early sign of market mood in the business sector. Opinion mining and machine learning can be used to discover the underlying sentiment. There's a link between Twitter sentiment and Bitcoin price changes in the future. Using the concept of Consensus clustering, this paper leverages Tweets collected during the COVID-19 timeframe to forecast early Bitcoin movements following the outbreak. Results from text datasets such as Twitter with various attributes, settings, and degrees show the superiority of the proposed consensus approach in predicting the BTC trend during and after the COVID-19 pandemic. |
| Keywords | Sentiment analysis+Social networking+ Market Mood+Bitcoin+Consensus clustering+Opinion mining |
| Year | 2021 |
| Conference | 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) |
| Publisher | IEEE |
| Publication dates | |
| Published | 27 Oct 2021 |
| Journal citation | pp. 0460-0465 |
| ISSN | 2644-3163 |
| ISBN | 9781665400664 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/IEMCON53756.2021.9623182 |
| Web address (URL) of conference proceedings | https://ieeexplore.ieee.org/xpl/conhome/9623060/proceeding |
| Web address (URL) | https://ieeexplore.ieee.org/document/9623182 |