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Developing a Comprehensive ESG Scoring System with Social Sentiment Integration
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Summary: This article explores the creation of a comprehensive ESG (Environmental, Social, and Governance) scoring system, enriched by the inclusion of social sentiment analysis. The project utilizes a Random Forest Regression Model, which is adept at combining predictions from numerous decision trees to improve accuracy and minimize overfitting. This model demonstrated a notable R2 Correlation of 26.1% and a modest Mean Absolute Average Error (MAAE) of 13.4%, surpassing other models in performance.
Insight: The incorporation of social sentiment into ESG assessments represents a significant evolution, integrating broader societal values into corporate evaluations. This method not only measures traditional metrics but also encapsulates the intricate public opinion on corporate conduct, providing a more comprehensive perspective of a company's societal impact.
| Scores | Value | Explanation |
|---|---|---|
| Objectivity | 5 | Content provides a balanced view on the development of ESG scoring, supported by data and model performance. |
| Social Impact | 4 | Content discusses a topic that influences public opinion on corporate responsibility and sustainability. |
| Credibility | 5 | Content is credible, discussing a model with demonstrated performance metrics. |
| Potential | 5 | Content has high potential to influence ESG evaluation practices and corporate behavior. |
| Practicality | 5 | Content is highly practical, offering a method directly applicable to ESG evaluations. |
| Entertainment Value | 2 | Content is informative but lacks typical entertainment elements. |