Creating a Social Sentiment Score for Carbon Credit Projects in Marginalized Communities

I. Introduction

In this Omdena Project, the team has developed a Social Performance Analysis Tool over an 8-week period. The outcome is an MVP dashboard designed to assess community reactions to ongoing carbon projects, providing a comprehensive performance score based on analyzed feedback.

The project addresses the critical need for community involvement in carbon neutrality initiatives, recognizing the importance of real-time feedback to enhance project implementation and success.

Marginalized communities are often the most affected by climate change, making their involvement in carbon neutrality initiatives both a priority and a challenge. This project seeks to amplify their voices and concerns in the broader conversation about sustainable environmental practices.

II. Problem Statement

The project tackles the complex issue of effectively gathering and interpreting the diverse sentiments of communities towards carbon projects, which is crucial for the projects’ success and community engagement.

Carbon neutrality plays a pivotal role in combating climate change, and this project contributes by providing insights into community sentiment, which can guide better project implementation.

The project underscores the importance of involving communities and businesses in carbon neutrality initiatives and the significance of offsetting activities like renewable energy projects and preserving forests.

III. Project Objectives

• Empowerment through Transition to Carbon Neutrality: The aim is to empower communities, especially those marginalized due to their environmental and social vulnerabilities, in their transition to carbon neutrality.
• Data and Sentiments Collection: By collecting data and sentiments, the project seeks to understand the views, concerns, and expectations of these communities regarding potential carbon credit projects.

IV. Project Scope

• In-depth Data Processing and Analysis: Using advanced tools for Named Entity Recognition and data crawling from social media platforms.
• Sentiment Analysis: Using VADER and ChatGPT, classifying sentiments into positive, negative, and neutral categories, and developing a sentiment score that reflects both emotional tone and engagement metrics.

V. Methodology

A multi-faceted methodology was adopted to ensure comprehensive sentiment analysis. This included:

• Document Crawling: Automated extraction of data from registries, allowing for efficient data gathering.
• Keywords Extraction: Leveraging Flair’s advanced NER capabilities to identify and extract pertinent keywords, enhancing the analysis process.
• Social Media Crawling: Utilizing tools and scripts to navigate and extract relevant data from social media giants like Facebook, Instagram, and Google Search.
• Sentiment Analysis: Implementing VADER and ChatGPT for nuanced sentiment analysis, classifying emotions into positive, negative, and neutral categories.
• Sentiment Scoring: Developing a holistic sentiment score that integrates emotional tone with engagement metrics, scaled to a 0-100 range for clarity.

VI. Project Outcomes

The project resulted in an MVP dashboard that provides a nuanced scoring system for community sentiment towards specific carbon projects, deployed on a cloud infrastructure. This tool is designed to deliver actionable insights that can significantly enhance the effectiveness of carbon projects.

VII. Relevance for the Partner

For the partner, this tool is a game-changer, offering a window into the community’s mindset. It is a critical step in ensuring that carbon projects not only take off but also land well with the intended beneficiaries. The dashboard’s integration is planned to be seamless and standalone, with future enhancements guided by partner feedback.

VIII. Future Directions

The MVP has shown great potential. Initial testing and validation indicate that the MVP is a robust tool for categorizing and scoring community sentiments. Future enhancements are set to focus on broadening language support, refining the app’s architecture, and improving the sentiment scoring algorithm for even greater accuracy and relevance.