Impact of ESG News Sentiment Analysis Based on Natural Language Processing on Investment Portfolio Performance

Environmental, Social, and Governance (ESG) investing has gained unprecedented momentum in global financial markets, driving the need for sophisticated analytical frameworks that can process vast amounts of unstructured information. This research presents a comprehensive investigation into the application of natural language processing techniques for ESG news sentiment analysis and its subsequent impact on investment portfolio performance. The study develops a multi-dimensional sentiment analysis model that extracts ESG-related information from financial news sources, incorporating advanced text mining algorithms to quantify sentiment scores across environmental, social, and governance dimensions. Through empirical analysis of portfolio performance metrics, the research demonstrates that ESG sentiment-driven investment strategies yield superior risk-adjusted returns compared to traditional approaches. The methodology integrates real-time news processing capabilities with portfolio optimization algorithms, enabling dynamic allocation decisions based on sentiment-derived ESG signals. Experimental results indicate a 50.8% improvement in Sharpe ratio and 17.3% reduction in portfolio volatility when incorporating ESG sentiment analysis. The findings contribute to the advancement of sustainable finance technology and provide practical insights for institutional investors seeking to enhance portfolio performance through alternative data integration.