Implements a fake news detection program using classifiers for Data Mining course at UoA.
The project is the categorization of text data by news articles and specifically the detection of fake news. The data contains 2 files in csv format (Fake.csv, True.csv)
Removed punctuation and made all letters uniform after dropped every null row
To analyse the preprocessed data it has to be represented in a numeric format by using:
- Bag of Words - one of the simplest word embedding approaches
- TF-IDF is a bag words that applies a regularization algorithm.
- Word vectors from Word2Vec model to create a vector representation for a sentence.
For every of the following classifiers there is a detailed analysis in the pytorch file
- Logistic Regression
- Naive Bayes
- Support Vector Machine
- Random Forests
- Voting Classifier
We evaluate performance of each method in test data using the following evaluation metrics:
- Accuracy score
- F1 score which is the weighted average of precision and recall and thus it is used especially for uneven class distribution problems.