Step Description
1. Load Dataset Load the dataset of news articles containing labeled real and fake news.
2. Preprocess Data Clean the text data (remove punctuation, stopwords, lowercase text, etc.).
3. Tokenize Text Split the text into individual tokens (words).
4. Vectorize Text Convert tokens into numerical representations, such as TF-IDF or Word Embeddings.
5. Train-Test Split Split the dataset into training and testing sets.
6. Build Model Select an NLP model, like Logistic Regression, Naive Bayes, or an LSTM/Transformer-based neural network.
7. Train Model Train the model on the training data to learn patterns in real vs. fake news.
8. Evaluate Model Test the model on the testing data and calculate metrics like accuracy, precision, recall, and F1 score.
9. Fine-Tune Model Optimize the model by adjusting hyperparameters or using techniques like cross-validation.
10. Deploy Model Save the model for deployment, allowing it to predict if new articles are fake or real.
11. Test on New Data Feed new articles to the deployed model and output the predicted label (real/fake).
# Step 1: LOAD DATA
- Load two datasets: one containing real news articles and the other containing fake news articles.
# Step 2: LABEL DATA
- Assign a label to each dataset:
- Label "1" for real news.
- Label "0" for fake news.
# Step 3: COMBINE DATA
- Merge both datasets into a single dataset for ease of processing.
# Step 4: PREPROCESS TEXT
- For each article in the dataset:
- Clean the text by:
- Converting to lowercase.
- Removing punctuation.
- Removing stopwords (common words with little meaning like "the", "is").
- Optional: Lemmatize or stem words to reduce them to their base form (e.g., "running" becomes "run").
# Step 5: CONVERT TEXT TO NUMERICAL FORMAT
- Use a technique to represent text numerically (so it can be used by a machine learning model):
- For example, apply TF-IDF (Term Frequency-Inverse Document Frequency), which assigns importance to words based on how frequently they appear in articles and across the dataset.
# Step 6: SPLIT DATA INTO TRAINING AND TESTING SETS
- Divide the dataset:
- Use a portion for training (e.g., 80%) to allow the model to learn from labeled examples.
- Use the remaining portion for testing (e.g., 20%) to evaluate how well the model can generalize to unseen data.
# Step 7: SELECT A MACHINE LEARNING MODEL
- Choose an appropriate model for classification (e.g., Logistic Regression, Naive Bayes, or a neural network):
- The model will learn to classify articles based on patterns in the text that are associated with real or fake news.
# Step 8: TRAIN THE MODEL
- Use the training data to train the model:
- The model will find patterns or correlations between word usage and labels (real or fake).
# Step 9: TEST THE MODEL
- Use the test set to measure the model’s accuracy:
- Predict labels (real or fake) for each article in the test set.
- Compare the predicted labels with the actual labels to calculate accuracy, precision, recall, and F1 score.
- These metrics will show how well the model distinguishes between real and fake news.
# Step 10: FINE-TUNE (IF NEEDED)
- Adjust settings (like model parameters) to improve performance if the accuracy or other metrics are low.
# Step 11: DEPLOY THE MODEL
- Save the trained model so it can be used to classify new articles in real-time.
# Step 12: MAKE PREDICTIONS ON NEW ARTICLES
- When a new article is given:
- Clean the article text in the same way as training data.
- Convert the text to the numerical format used in training.
- Use the model to predict the label:
- If the label is "1," the article is likely real.
- If the label is "0," the article is likely fake.
# OUTPUT the predicted label (Real/Fake) for new articles.