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Menu iconMenu iconNLP with Transformers: Fundamentals and Core Applications
NLP with Transformers: Fundamentals and Core Applications
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Project 3: Customer Feedback Analysis Using Sentiment Analysis
1. Why Sentiment Analysis?
2. What Will You Learn?
3. Step 1: Setting Up the Environment
4. Step 2: Loading and Preparing the Dataset
5. Step 3: Fine-Tuning BERT for Sentiment Analysis
6. Step 4: Evaluating the Model
7. Step 5: Testing with Real Customer Feedback
Conclusion
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Project 3: Customer Feedback Analysis Using Sentiment Analysis

6. Step 4: Evaluating the Model

Evaluate the trained model on the test set to measure its performance.

from sklearn.metrics import classification_report

# Predict on the evaluation set
predictions = trainer.predict(eval_dataset)

# Convert predictions to labels
predicted_labels = predictions.predictions.argmax(-1)

# Print classification report
print(classification_report(eval_dataset['label'], predicted_labels))

Code breakdown:

1. Import and Setup

from sklearn.metrics import classification_report

This imports scikit-learn's classification report tool for evaluating model performance.

2. Making Predictions

predictions = trainer.predict(eval_dataset)
predicted_labels = predictions.predictions.argmax(-1)

This code:

  • Uses the trained model to make predictions on the evaluation dataset
  • Converts the raw predictions into class labels using argmax (choosing the highest probability class)

3. Evaluation

print(classification_report(eval_dataset['label'], predicted_labels))

This generates a report comparing the true labels with predicted labels, showing metrics like precision, recall, and F1-score for each sentiment class. This evaluation step is crucial for understanding how well the model performs on unseen data before deploying it for real customer feedback analysis.

6. Step 4: Evaluating the Model

Evaluate the trained model on the test set to measure its performance.

from sklearn.metrics import classification_report

# Predict on the evaluation set
predictions = trainer.predict(eval_dataset)

# Convert predictions to labels
predicted_labels = predictions.predictions.argmax(-1)

# Print classification report
print(classification_report(eval_dataset['label'], predicted_labels))

Code breakdown:

1. Import and Setup

from sklearn.metrics import classification_report

This imports scikit-learn's classification report tool for evaluating model performance.

2. Making Predictions

predictions = trainer.predict(eval_dataset)
predicted_labels = predictions.predictions.argmax(-1)

This code:

  • Uses the trained model to make predictions on the evaluation dataset
  • Converts the raw predictions into class labels using argmax (choosing the highest probability class)

3. Evaluation

print(classification_report(eval_dataset['label'], predicted_labels))

This generates a report comparing the true labels with predicted labels, showing metrics like precision, recall, and F1-score for each sentiment class. This evaluation step is crucial for understanding how well the model performs on unseen data before deploying it for real customer feedback analysis.

6. Step 4: Evaluating the Model

Evaluate the trained model on the test set to measure its performance.

from sklearn.metrics import classification_report

# Predict on the evaluation set
predictions = trainer.predict(eval_dataset)

# Convert predictions to labels
predicted_labels = predictions.predictions.argmax(-1)

# Print classification report
print(classification_report(eval_dataset['label'], predicted_labels))

Code breakdown:

1. Import and Setup

from sklearn.metrics import classification_report

This imports scikit-learn's classification report tool for evaluating model performance.

2. Making Predictions

predictions = trainer.predict(eval_dataset)
predicted_labels = predictions.predictions.argmax(-1)

This code:

  • Uses the trained model to make predictions on the evaluation dataset
  • Converts the raw predictions into class labels using argmax (choosing the highest probability class)

3. Evaluation

print(classification_report(eval_dataset['label'], predicted_labels))

This generates a report comparing the true labels with predicted labels, showing metrics like precision, recall, and F1-score for each sentiment class. This evaluation step is crucial for understanding how well the model performs on unseen data before deploying it for real customer feedback analysis.

6. Step 4: Evaluating the Model

Evaluate the trained model on the test set to measure its performance.

from sklearn.metrics import classification_report

# Predict on the evaluation set
predictions = trainer.predict(eval_dataset)

# Convert predictions to labels
predicted_labels = predictions.predictions.argmax(-1)

# Print classification report
print(classification_report(eval_dataset['label'], predicted_labels))

Code breakdown:

1. Import and Setup

from sklearn.metrics import classification_report

This imports scikit-learn's classification report tool for evaluating model performance.

2. Making Predictions

predictions = trainer.predict(eval_dataset)
predicted_labels = predictions.predictions.argmax(-1)

This code:

  • Uses the trained model to make predictions on the evaluation dataset
  • Converts the raw predictions into class labels using argmax (choosing the highest probability class)

3. Evaluation

print(classification_report(eval_dataset['label'], predicted_labels))

This generates a report comparing the true labels with predicted labels, showing metrics like precision, recall, and F1-score for each sentiment class. This evaluation step is crucial for understanding how well the model performs on unseen data before deploying it for real customer feedback analysis.

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