Fake News Detector

Compiled.ipynb is to show the whole flow of our analysis (without output). The process is as follow :
EDA and Preprocessing is the exploration to understand our dataset prior to analysis and creation of dataframes of the policitcs dataset
TopicModelling is used to generate the topic distribution for both politics and entertainment domain
Emotion_Sentiment_Pos is used to genereate the emotion distribution, sentiment distribution and pos grouping for politics domain
Named Entity Recognition is used to generate the entity type count for Location, Persons and Organisations
CrossDomain_Emotion_Sentiment_POS is used to genereate the emotion distribution, sentiment distribution, pos grouping and entity type count for entertainment domain that will be used for testing for cross domomain model
Classification shows the model training , testing and validation process. It also shows the type of models used and the respective data used for both specific and cross domain classifiers
process_text is to process the dataset from the entertainment dataset.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
ClassificationPickles		ClassificationPickles
Classification.ipynb		Classification.ipynb
Compiled.ipynb		Compiled.ipynb
CrossDomain_Emotion_Sentiment_POS.ipynb		CrossDomain_Emotion_Sentiment_POS.ipynb
EDA and Preprocessing.ipynb		EDA and Preprocessing.ipynb
Emotion_Sentiment_Pos.ipynb		Emotion_Sentiment_Pos.ipynb
Named Entity Recognition.ipynb		Named Entity Recognition.ipynb
README.md		README.md
TopicModelling.ipynb		TopicModelling.ipynb
process_text.ipynb		process_text.ipynb
topicModel14.pickle		topicModel14.pickle

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