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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