Conventional Research Project submitted for the Computing Project (COMP90055 - 25 credit points) of Unimelb.

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In this project, machine learning methods and neural networks are trained to classify and analyze sentiment of fake and real news on social networks.

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All the codes are in the code folder;

The files in the relative path used in code are in the Desktop/dataset;

All the datasets are in Desktop/dataset and we also provide dataset.zip for downloading easily.

Harvester: harvester_fakenewsnet.py and harvester_credbank.py

Python machine learning: ML_ver.py, including data-preprocessing, featrue selection, Naive Bayes model training, Decision Tree model training, Random Forest model training and sentimental analysis.

Neural network model:BiLSTM.ipynb and Glove method:https://nlp.stanford.edu/projects/glove/

Sentimental analysis:Feature_Sentiment.ipynb which is used after all data being processed.

Test data set process: Testset_Preprocessing.ipynb which is only for data pre-processing used for test data.