Correlation between BTC market movement based on twitter feeds related to bitcoin

by Tauseef Bashir

Executive summary

With the growth of market capitalization of cryptocurrencies (increased from $17 billion in 2017 to $2.25 trillion in 2021), cryptocurrencies remain incredibly volatile, with their value impacted by a multitude of factors: market trends, politics, technology…and Twitter. There have been instances where digital assets prices were affected by tweets by famous personalities and the famous influencers.

I plan to analyze trends over time, particularly the impact of social media on the price volatility of a crypto asset, such as Bitcoin (BTC).

Research Question

Research Question: Twitter Sentiment Analysis for Predicting Digital Assets Price Movements

Data Sources

BTC tweets dataset: https://www.kaggle.com/datasets/kaushiksuresh147/bitcoin-tweets

BTC historical data: https://www.kaggle.com/datasets/mczielinski/bitcoin-historical-data

Results

Please see the attached notebooks for detailed graphs and conclutions.

Notebooks

Please find attached the following two notebooks for bitcoin twitter and financial analysis:

• BTC-Twitter-EDA.ipynb
• BTC-analysis-using-Prophet-Pycarat.ipynb
• Final Modelling-BTC-tweets-sentiment-market-effect-LSTM.ipynb

References

1. Time series prediction using Prophet in Python by Renu Khandelwal
2. Housing pices EDA and Prediction by Ruchi Bhatia
3. 88.9 r2_score with pycaret by Kerem Yucedag
4. Pycaret documentation
5. https://keras.io/api/layers/recurrent_layers/lstm/
6. https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM
7. https://www.tensorflow.org/tutorials/structured_data/time_series