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Database comments on Telegram channels related to cryptocurrencies with sentiments
Kia Jahanbin1, Mohammad Ali Zare Chahooki2, Mahdi Yazdian-Dehkordi1
1Department of Computer Engineering, Yazd University, Yazd, Iran.
BMC Research Notes
|May 14, 2024
Summary
This study analyzes cryptocurrency market sentiment from influential Telegram channels using a hybrid deep learning model (HDRB). Findings offer valuable insights for understanding market behavior and managing investment portfolios.
Area of Science:
- Natural Language Processing
- Machine Learning
- Financial Market Analysis
Background:
- Telegram channels are increasingly influential in cryptocurrency markets.
- Traditional platforms like Twitter have limitations for expert opinion sharing.
- Expert sentiment analysis on cryptocurrencies is crucial for market prediction.
Purpose of the Study:
- To extract and analyze expert opinions from popular Telegram channels regarding cryptocurrency.
- To evaluate the effectiveness of the HDRB model for sentiment analysis in this domain.
- To create a valuable dataset of expert cryptocurrency sentiments.
Main Methods:
- Data collection via Telegram API from December 2023 to March 2024.
- Utilized a hybrid deep learning model (HDRB: RoBERTa, BiGRU, attention layer) for sentiment analysis.
- Dataset includes comment text, date, views, and sentiment polarity.
Main Results:
- Extracted opinions from over ten influential Telegram channels.
- Applied the HDRB model to analyze sentiment polarity of expert cryptocurrency analyses.
- Developed a unique dataset rare in the cryptocurrency sentiment analysis field.
Conclusions:
- The HDRB model effectively analyzes expert sentiments on cryptocurrencies from Telegram.
- This research provides a valuable resource for understanding market dynamics.
- Findings aid in informed investment and portfolio management decisions.
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