M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing Further, sentiment analysis results were incorporated as features for classification. Three models were trained with sentiment features: Model 1 was a Bi-LSTM with sentiment score concatenation, Model 2 was a Bi-LSTM with two dense layers, and Model 3 involved a Bi-LSTM with five dense layers. While Model 3 achieved the highest accuracy on the test set, the marginal improvement in accuracy compared to Model 1 suggests that the added complexity is not justified. Further experimentation with LIME was built to assess the explainability of the models. The Bidirectional LSTM model features two Bidirectional LSTM layers, effectively capturing temporal dependencies within text data while balancing complexity and computational efficiency. ReLU activation functions are employed in dense layers to mitigate the vanishing gradient problem, with sigmoid activation utilized in the output layer for binary classification. Dropout layers are strategically incorporated to prevent overfitting by randomly dropping units during training, with dropout rates adjusted based on model complexity to maintain generalizability. Adding three or four layers to the model brings unnecessary complexity without significant accuracy improvements, resulting in diminishing returns. The model optimization process involved adjusting hyperparameters and architectural elements to improve performance and efficiency. This optimization included fine-tuning parameters such as learning rate, batch size, dropout rates, and optimizer choice through iterative adjustments based on training and evaluation results. The number of nodes and hidden layers was optimized to balance model complexity and computational efficiency, with multiple Bidirectional LSTM layers being incorporated to capture temporal dependencies. Activation functions, including ReLU, were chosen to introduce non-linearity and mitigate the vanishing

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