Elliot Cao
ABSTRACT
This research explores the intricate relationship between sentiment analysis, stock market dynamics, and Environmental, Social, and Governance (ESG) based investment analytics, harnessing sentiment as a predictive tool for stock price movements. Leveraging Twitter data, Natural Language Processing (NLP), TextBlob, and the scikit-learn RandomForestRegressor, in combination with machine learning algorithms, the study evaluates public sentiment's impact on stock prices, offering valuable insights to investors and risk managers. Moreover, the findings elucidate the potential to enhance ESG-based investment analytics by incorporating sentiment-derived insights into investment decision-making processes, which is particularly pertinent given the increasing market focus on sustainable investing. Experimental results unveil the potential of sentiment analysis in forecasting stock price changes and augmenting ESG investment strategies, underlining its utility as both a forecasting instrument and a risk management mechanism. However, the research also identifies challenges, including limitations of the Twitter API and the need for data refinement. Strategies to address these challenges are discussed, emphasizing the importance of diversifying data sources and enhancing data quality. This study advances our understanding of sentiment analysis in financial markets and its applicability to ESG-based investment analytics, offering data-driven guidance to navigate the complexities of the stock market landscape. Ultimately, it highlights the promising prospect of integrating social media sentiment analysis with machine learning for more informed stock market predictions, risk management, and sustainable investment strategy formulation.
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Index Terms
- A Data-Driven Analytical Framework for ESG-based Stock Investment Analytics using Machine Learning and Natural Language Processing
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