This project is Sensational News Detection. This was submitted as a CSI6900 Graduate project under the guidance of Dr. Diana Inkpen. We have built a Jupyter Notebook using which you can run the code. Following files are used:

1. ObjectiveSensationalist_Baseline.py - This file is used to demonstrate the baseline model i.e. Bernoulli's Naive Bayes model.
2. SensationalismNews_Classification.ipynb - This is a jupyter notebook built on Google Colaboratory tool. This contains all the existing outputs. And if you want to run then please follow the following instructions.

In order for the model to train, please download the following files in the /data/ folder:

1. glove.6B.100d.txt from

https://drive.google.com/open?id=1-FLa3Sfmn5XWyidMs-155tLGAMCWXDnV

In order to test the pre-trained models, please follow the following steps:

1. Download the following file in the /data/ folder:

https://drive.google.com/open?id=1-2ex_uj839Cfd0X1h03DMkKXRnFl09fm

2. In the existing jupyter notebook run the following code in the last cell of the jupyter notebook:

load_model = pickle.load(open('data/finalized_model_300D.sav')) x_test = feature_test('data/sample_test_data.csv') pred_result = load_model(x_test) pred_res_final = finalized_result(pred_result)