Code icon

The App is Under a Quick Maintenance

We apologize for the inconvenience. Please come back later

Menu iconMenu iconNLP with Transformers: Fundamentals and Core Applications
NLP with Transformers: Fundamentals and Core Applications

Project 3: Customer Feedback Analysis Using Sentiment Analysis

2. What Will You Learn?

Through this project, you will develop several key skills and capabilities:

  • Gain experience in preprocessing text data for sentiment analysis:
    • Learn data cleaning techniques for handling raw text
    • Master tokenization and text normalization methods
    • Understand how to handle special characters and formatting
  • Fine-tune a pre-trained BERT model for sentiment classification:
    • Learn the principles of transfer learning with BERT
    • Understand model architecture and hyperparameter tuning
    • Master techniques for avoiding overfitting during fine-tuning
  • Learn how to evaluate the model using accuracy, precision, recall, and F1-score:
    • Understand different evaluation metrics and their importance
    • Learn to interpret confusion matrices
    • Master cross-validation techniques for robust evaluation
  • Build a practical application to analyze customer sentiment in real-world scenarios:
    • Develop scalable solutions for processing large volumes of feedback
    • Create interactive interfaces for sentiment analysis
    • Implement real-time sentiment monitoring systems

2. What Will You Learn?

Through this project, you will develop several key skills and capabilities:

  • Gain experience in preprocessing text data for sentiment analysis:
    • Learn data cleaning techniques for handling raw text
    • Master tokenization and text normalization methods
    • Understand how to handle special characters and formatting
  • Fine-tune a pre-trained BERT model for sentiment classification:
    • Learn the principles of transfer learning with BERT
    • Understand model architecture and hyperparameter tuning
    • Master techniques for avoiding overfitting during fine-tuning
  • Learn how to evaluate the model using accuracy, precision, recall, and F1-score:
    • Understand different evaluation metrics and their importance
    • Learn to interpret confusion matrices
    • Master cross-validation techniques for robust evaluation
  • Build a practical application to analyze customer sentiment in real-world scenarios:
    • Develop scalable solutions for processing large volumes of feedback
    • Create interactive interfaces for sentiment analysis
    • Implement real-time sentiment monitoring systems

2. What Will You Learn?

Through this project, you will develop several key skills and capabilities:

  • Gain experience in preprocessing text data for sentiment analysis:
    • Learn data cleaning techniques for handling raw text
    • Master tokenization and text normalization methods
    • Understand how to handle special characters and formatting
  • Fine-tune a pre-trained BERT model for sentiment classification:
    • Learn the principles of transfer learning with BERT
    • Understand model architecture and hyperparameter tuning
    • Master techniques for avoiding overfitting during fine-tuning
  • Learn how to evaluate the model using accuracy, precision, recall, and F1-score:
    • Understand different evaluation metrics and their importance
    • Learn to interpret confusion matrices
    • Master cross-validation techniques for robust evaluation
  • Build a practical application to analyze customer sentiment in real-world scenarios:
    • Develop scalable solutions for processing large volumes of feedback
    • Create interactive interfaces for sentiment analysis
    • Implement real-time sentiment monitoring systems

2. What Will You Learn?

Through this project, you will develop several key skills and capabilities:

  • Gain experience in preprocessing text data for sentiment analysis:
    • Learn data cleaning techniques for handling raw text
    • Master tokenization and text normalization methods
    • Understand how to handle special characters and formatting
  • Fine-tune a pre-trained BERT model for sentiment classification:
    • Learn the principles of transfer learning with BERT
    • Understand model architecture and hyperparameter tuning
    • Master techniques for avoiding overfitting during fine-tuning
  • Learn how to evaluate the model using accuracy, precision, recall, and F1-score:
    • Understand different evaluation metrics and their importance
    • Learn to interpret confusion matrices
    • Master cross-validation techniques for robust evaluation
  • Build a practical application to analyze customer sentiment in real-world scenarios:
    • Develop scalable solutions for processing large volumes of feedback
    • Create interactive interfaces for sentiment analysis
    • Implement real-time sentiment monitoring systems