PreBit — A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Zou, Yanzhao; Herremans, Dorien

doi:10.1016/j.eswa.2023.120838

Cited by 6 publications

(1 citation statement)

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“…Zou and Herremans [12] examine the potential of using Twitter data, beyond simple sentiment analysis, to predict Bitcoin's extreme price movements, attributed in part to influential figures' tweets. A novel multimodal deep learning approach is introduced, which integrates candlestick patterns, correlated asset prices, technical indicators, and Twitter content.…”

Section: Introductionmentioning

confidence: 99%

A Descriptive-Predictive–Prescriptive Framework for the Social-Media–Cryptocurrencies Relationship

Băroiu,

Bâra

2024

Electronics

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The research presented in this paper is the first to introduce a thorough Descriptive-Predictive–Prescriptive (DPP) Framework for comprehending the interaction between social media and cryptocurrencies. Recognizing the underexplored domain of the social-media–cryptocurrency interaction, we delve into its many aspects, better understanding present dynamics, forecasting potential future trajectories, and prescribing best solutions for stakeholders. We evaluate social media speech and behavior connected to cryptocurrencies using big data analytics, translating raw data into meaningful insights using Natural Language Processing (NLP) techniques like sentiment analysis. When applied to an experimental dataset, the DPP nets superior results compared to the baseline approach, displaying an improvement of 3.44% of the Root Mean Square Error (RMSE) metric and 4.59% of the Mean Absolute Error (MAE) metric. The unique DPP framework enables a more in-depth assessment of social media’s influence on cryptocurrency trends, and lays the path for strategic decision-making in this nascent but rapidly developing field of study.

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Section: Introductionmentioning

confidence: 99%