NewsDetector.py

#!/usr/bin/env python
# coding: utf-8

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#Necessary imports
import numpy as np
import pandas as pd
import itertools
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import PassiveAggressiveClassifier
from sklearn.metrics import accuracy_score, confusion_matrix


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#Reading the data
df=pd.read_csv('Desktop/news.csv')

#Get shape and head
df.shape
df.head()


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#Get the labels
labels=df.label
labels.head()


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#Split the dataset into training and testing sets
x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)


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#Initialize a TfidfVectorizer with stop words from the English language and a maximum document 
    #frequency of 0.7 (terms with a higher document frequency will be discarded). Stop words are 
    #the most common words in a language that are to be filtered out before processing the natural 
    #language data. And a TfidfVectorizer turns a collection of raw documents into a matrix of TF-IDF features.


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#Initialize a TfidfVectorizer
tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)

#Fit and transform train set, transform test set
tfidf_train=tfidf_vectorizer.fit_transform(x_train) 
tfidf_test=tfidf_vectorizer.transform(x_test)


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#Next, initialize a PassiveAggressiveClassifier. This is. We’ll fit this on tfidf_train and y_train.
   #Then, we’ll predict on the test set from the TfidfVectorizer and calculate the accuracy with 
   #accuracy_score() from sklearn.metrics.


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#Initialize a PassiveAggressiveClassifier
pac=PassiveAggressiveClassifier(max_iter=50)
pac.fit(tfidf_train,y_train)

#Predict on the test set and calculate accuracy
y_pred=pac.predict(tfidf_test)
score=accuracy_score(y_test,y_pred)
print(f'Accuracy: {round(score*100,2)}%')


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#Recieved an accuracy of 92.74% with this model. Finally, let’s print out a confusion matrix to gain 
    #insight into the number of false and true negatives and positives.


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#Build confusion matrix
confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])


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#So with this model, we have 588 true positives, 587 true negatives, 50 false positives, and 42 false negatives.

#SUMMARY:
    #Goal was to detect fake news with Python. We took a political dataset, 
    #implemented a TfidfVectorizer, initialized a 
    #PassiveAggressiveClassifier, and fit our model. We ended 
    #up obtaining an accuracy of 92.74% in magnitude.


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