an attempt to tackle the fake news problem using MATLAB
we broke the problem into a classification problem to determine whether articles were related to or biased against a certain headline.
demoObjects.mat contains the MATLAB objects for the Naive Bayes Classifier and the sentiment map.
doesAgree.m takes a headline and a body as strings (along with the learned model and sentiment map), and determines which of the four categories it falls under: unrelated, agree, disagree, or discuss
checkAllAgree takes a table with a Headline field, an articleBody field, and an observed stance field, outputting a table of results with the given stance along with the computed stance
getGrade takes these results and calculates a percentage score for the number of correctly classified (headline, article) pairs.
getFullGrade runs everything without the need to load anything. This will give the full score as long as the demoObjects.mat and competition test csvs are in the folder
competitionResults.mat contains the results from running getFullGrade over the whole competition dataset.
we should consider using ml to categorize the relevant articles into the correct bias categories. This involves finding another relevant input parameter. Since headlines are rather short, this is rather difficult.