Bitcoin price prediction using sentiment analysis

Aim: To explore the causal relationship between social media sentiment, related behavioral factors, and bitcoin priceperformance, and to develop predictive models with higher accuracy in forecasting the bitcoin price by incorporatingsentiment analysis.Methods: We used the Valence Aware Dictionary and sEntiment Reasoner module to perform a sentiment analysisof 896,464 Twitter posts (tweets) published between November and December 2021, which we collected via webscraping. We created several forecasting models using the average daily sentiment polarity, the average daily numberof tweets, and search interest for “bitcoin” on Google and Wikipedia as input variables. We predicted future bitcoinprices using vector autoregression (VAR), Prophet, and long short-term memory (LSTM) artificial neural network modelsand evaluated their predictive accuracy using the mean absolute percentage error (MAPE) as a performance measure.Results: The results suggest a Granger causal relationship between social media sentiment and bitcoin prices. Thestandard VAR model achieved a MAPE of 8%, while the LSTM model had a lower error rate of 5%. The Prophet modelhad a MAPE of 11%.Conclusion: Our results underline the highly speculative nature of bitcoin, especially in times of high prices. The inclusionof behavioral variables in the development of bitcoin price prediction models significantly improved their predictionaccuracy, with the LSTM neural network model proving to be an extremely effective tool in this sense.