Sentiment-driven cryptocurrency forecasting

Analyzing LSTM, GRU, Bi-LSTM, and temporal attention model (TAM)

Bibliographic Data

ID4659151
AuthorsPhumudzo Lloyd Seabe (0000-0002-7538-2547, Sefako Makgatho Health Sciences University, corresponding author), Claude Rodrigue Bambe Moutsinga (0000-0003-1475-5124, Sefako Makgatho Health Sciences University), Edson Pindza (0000-0003-3207-8067, University of South Africa)
Year2025
Volume15
Issue1
Publication date2025-05-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Network Analysis and Mining (JOURNAL)
Journal identifiersISSN: 1869-5450 • E-ISSN: 1869-5469
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-025-01463-6
OpenAlexW4410357792
LanguageEN
Citations received1
References cited58

Predicting cryptocurrency prices is challenging due to market volatility and external influences like social media sentiment. This study integrates Twitter sentiment analysis with deep learning models (LSTM, GRU, Bi-LSTM, and Temporal Attention Model) to enhance Bitcoin price forecasting. Sentiment features were extracted using VADER and RoBERTa, with findings showing that RoBERTa-based models significantly outperform VADER. Bi-LSTM (RoBERTa) achieved the lowest MAPE of 2.01%, demonstrating the effectiveness of deep contextual embeddings. SHAP analysis identified Sentiment Momentum, RoBERTa Compound Score, and VADER Negativity Score as key predictors of price movements. These results highlight the value of sentiment-driven forecasting and provide insights for traders, investors, and researchers

Computer security · Cryptocurrency · Machine learning · Sentiment analysis · Complex Network Analysis Techniques · Complex Systems and Time Series Analysis · Computer Science · Opinion Dynamics and Social Influence · Artificial Intelligence

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