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news-analysis

News Classification and Sentiment Analysis

In this notebook, we'll be analyzing a news data set from this website (https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/)

tl;dr:

Libraries used:

sklearn specifically PassiveAggressiveClassifier among many more
re
The VADER lexicon
and many more standard libraries

Key Takeaways:

We trained a PassiveAgressiveClassifier and achieved an accuracy of about 94% (with precision and recall ~93%)
Preformed sentiment analysis and saw that FAKE articles were the usually the most positive or negative articles present in the dataset

Things I Learned:

Fake news is really scary and often seems real
How much choice of model impacts accuracy

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News Classification and Sentiment Analysis

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