🚀 Overview: Leveraging AI and machine learning for customer feedback analysis. Analyzing tweets to understand sentiment, improve customer support, and showcase language model capabilities.
📊 Data Source: Kaggle's "Customer Support on Twitter" dataset provides a rich collection of customer interactions on social media.
🛠️ Technologies:
- Languages: Python, R, SQL
- Libraries: NLTK, spaCy, Pandas, Scikit-learn
- Tools: Kaggle API
🔍 Project Goals:
- Sentiment Analysis: Utilize NLP for customer tweet sentiment analysis.
- Customer Support Insights: Extract valuable insights for support strategy enhancement.
- Language Model Integration: Explore Language Model concepts in feedback analysis.
📂 Project Structure:
- data/: Dataset files
- notebooks/: Jupyter notebooks for exploration and analysis
- scripts/: Python and R scripts for data processing and analysis
🌐 How to Use:
- Clone the repository.
- Set up Kaggle API key as instructed in the README.
- Run notebooks and scripts for analysis.
📈 Contributions: Contributions and feedback are welcome! Whether in NLP models, data visualization, or analysis scripts.
📧 Contact: For questions or collaborations, feel free to reach out.
Let's uncover the stories in customer feedback! 🎤📊