Sentiment-driven approach to refine stock price prediction

Stock price values are known for their volatility due to multiple factors making their predictability a difficult task.  As social media posts and news can be considered as one of the major factors in price change, we aim in this paper to predict the next-day stock price of 4~different companies, using both social media and financial datasets that range from September 30, 2021, to September 30, 2022, as inputs.  The datasets go through a preprocessing pipeline that includes sentiment analysis methods, where tweets are classified by employing TextBlob and finetuned RoBERTa to extract new features.  The best model produces a 93% R$^2$ score and an RMSE value of 1.35.

фондовий ринок
stock price
глибоке навчання
емоційно-смисловий аналіз
textBlob
finetuned Bert
LSTM
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