Fake-News-Detection-using-Twitter

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Fake-News-Detection-using-Twitter

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Data Science, Python, R, Machine Learning, Text Mining, Sentiment Analysis

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# Fake-News-Detection-using-Twitter
Data Science, Python, R, Machine Learning, Text Mining, Sentiment Analysis

This project was done as a part of INFM750 course - From Data to Insights which involves extracting 
tweets from twitter based on 2 recent events:

1. Las Vegas Shooting - Oct 1, 2017
2. Hurricane Harvey - Aug 17, 2017 - Sept 3, 2017

Tweets were extracted using Twitter's Streaming API in Python. (ExtractTweets-checkpoint.ipynb)
The dataset was created and cleaned using Python in Jupyter Notebook.(Graphs-checkpoint.ipynb)
Used NLTK package in Python to tokenize tweets, regex package was used to perform initial text mining 
and textblob package was used to perform sentiment analysis of the tweet text to classify it as 
positive, negative and neutral.

Executed all machine learning models in R.

The pdf contains the dataset description, exploratory data analysis, data cleaning steps, methodology 
and model accuracy information.

FinalDataset is the final dataset that is created along with the labels from 2 annotators.

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