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Related Experiment Video

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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.

Keywords:

Cryptocurrencies Sentiment analysis Telegram Trend prediction

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Published on: September 11, 2021

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.