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Information × Registration Number 2124U004711, Article popup.category Препринт Title popup.author Onyshkiv Taras popup.publication 01-01-2024 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4838 popup.publisher Description The thesis discusses the possibility of utilizing Twitter sentiment to forecast the stock price dynamics, providing an innovative way of combining social media analytics with the conventional stock market analysis. With digital platforms becom- ing more powerful in shaping financial markets, this study uses live Twitter data to assess public sentiment, utilizing advanced machine learning methods to predict stock price movements. This study employs a blend of sentiment analysis models specifically de- signed for financial contexts to analyze tweets regarding stock markets for sentiment polarity. The thesis introduces a predictive model that not only analyses sentiment of tweets, but also quantitatively incorporates this sentiment with traditional stock price data to predict market movements. The methodology involves the collection of a large dataset of tweets, senti- ment analysis, and the testing of predictive power of these sentiments against actual stock market performance. The results are verified using several machine learning models, which are tested for their predictive prowess of stock price directions utiliz- ing Twitter sentiment data. The results reveal that Twitter sentiment can bring about a considerable im- provement in stock returns prediction providing a more interactive and instant com- plement to traditional predictive models. By offering empirical evidence on how social media sentiment analysis enhances stock price prediction, this study benefits the financial industry and helps investors and analysts to make the right decisions. popup.nrat_date 2025-05-09 Close