Fake-News-Detector

This project is a Fake News Detection and Classification system that leverages advanced natural language processing and machine learning techniques, including TF-IDF, BERT, and sentiment analysis. The system is designed to identify and classify news articles as either real or fake based on their content.

Example Data for Fake News Detection

Headline	Content	Label
Illinois House passes $5 billion tax package, spending plan	CHICAGO (Reuters) - Illinois’ Democratic-controlled House of Representatives passed big, permanent i...	FAKE =0
AT&T, Time Warner and the Death of Privacy	AT&T, Time Warner and the Death of Privacy By Amy Goodman and Denis Moynihan AT&T plans to buy Ti...	REAL=1

Performance Analysis:

Overview

The Fake News Detection project includes the following components:

• Data Preprocessing: Cleans and prepares the text data for feature extraction. Handles missing values and non-string data entries.
• Feature Extraction:
  • TF-IDF (Term Frequency-Inverse Document Frequency): Converts text into numerical features capturing the importance of words in documents.
  • BERT Embeddings: Utilizes pre-trained BERT models to obtain contextual embeddings for better text representation.
  • Sentiment Analysis: Incorporates sentiment scores using VADER and TextBlob to capture the emotional tone of the text.
• Model Training: Builds and trains a neural network model using TensorFlow and Keras to classify news articles.
• Evaluation: Evaluates model performance with metrics such as confusion matrix, classification report, ROC curve, and accuracy.

Dataset

The dataset used for training is the Fake News Classification Dataset from Kaggle. It contains news articles labeled as real or fake, providing the necessary data for training and evaluating the classification model.

Evaluation Metrics

• Confusion Matrix: Displays the performance of the classification model.
• Classification Report: Provides precision, recall, and F1-score for each class.
• ROC Curve: Plots the receiver operating characteristic curve.
• Accuracy: Shows the accuracy score of the model.

Features

• Preprocessing: Cleans text data and handles edge cases.
• Feature Engineering: Extracts TF-IDF features, BERT embeddings, and sentiment scores.
• Model Training: Implements a neural network for classification and evaluates its performance.
• Visualization: Plots confusion matrix, classification report, ROC curve, and accuracy metrics.

Usage

1. Clone the repository.
2. Download Dataset from Kaggle
3. Run pip3 install -r requirements.txt.
4. Run python main.py.

Future Steps

• Fine-Tuning the Model: Further improve model performance by experimenting with different hyperparameters, architectures, and regularization techniques.
• Multi-Modal Model: Extend the system to process images as well by integrating image data with text data, enabling a multi-modal approach to fake news detection.