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The dissertation aims to investigate the relationship between public mood, as reflected in textual data from sources such as news articles and social media posts, and its influence on investor behavior in financial markets

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The dissertation aims to investigate the relationship between public mood, as reflected in textual data from sources such as news articles and social media posts, and its influence on investor behavior in financial markets. The research seeks to address the following key questions: How does sentiment analysis of textual data from various sources, such as news articles and social media posts, enable the measurement of public mood in financial markets? To what extent does public mood, as derived from sentiment analysis, affect investor decision-making in financial markets? How does it influence trading behaviors, investment strategies, and risk appetite? Can sentiment analysis provide predictive insights into market trends, stock price movements, and other financial market variables? What is the predictive power of public mood in forecasting short-term and long-term market outcomes? How do different sentiment analysis techniques, such as lexicon-based methods, machine learning algorithms, and deep learning models, compare in their accuracy and effectiveness for analyzing public mood in financial contexts? What ethical considerations and potential biases should be taken into account when using sentiment analysis to inform investment decisions and market analysis? How can the findings from sentiment analysis be practically applied in the context of financial markets? What are the potential implications for investors, financial analysts, and policymakers? By addressing these questions, the research seeks to contribute to a better understanding of the role of public mood in shaping investor behavior and market outcomes, as well as the potential applications of sentiment analysis in financial decision-making. The project will utilize real-world financial data and employ advanced sentiment analysis techniques to provide valuable insights for the finance industry and contribute to the emerging field of sentiment-driven investment strategies.