Work

Sentiment and Interest Detection in Social Media Using GPT-based Large Language Models

Machine Learning
NLP
Data Analysis
GPT Models
Flutter
Data Visualization

Comprehensive research leveraging GPT models for social media sentiment analysis and topic detection, presented at HBCU Emerging Technologies & Innovation Showcase 2023.

Dashboard visualization showing sentiment and interest detection insights.

This research explores sentiment analysis and interest/topic detection using cutting-edge GPT-based language models, including ChatGPT-3.5 and gpt4All. The project highlights innovative approaches for extracting meaningful insights from social media posts.

Key Contributions

  • GPT Model Integration: Utilized state-of-the-art Generative Pre-trained Transformers (GPT) to analyze social media data.
  • Dashboard Development: Created an interactive web-based dashboard to visualize users’ primary interest zones and expressed sentiments.
  • Data Processing: Implemented robust pipelines for data collection, processing, and analysis to ensure accurate insights.
  • Technological Showcase: Represented Clark Atlanta University at the HBCU Emerging Technologies & Innovation Showcase 2023.
  • Award Recognition: Earned the “Best in Class Award” for innovation and impact.

Skills Utilized

  • Machine Learning & NLP: Developed models to detect sentiments and topics from text data.
  • Data Analysis & Processing: Ensured comprehensive data handling for reliable output.
  • Flutter Development: Built front-end components for the interactive dashboard.
  • Data Visualization: Created clear and insightful visual representations of data.
  • Object-Oriented Design: Applied structured design principles for modular and maintainable code.

This project underscores my ability to leverage advanced AI models, develop impactful solutions, and contribute to academic and technological showcases.