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Fake news classifier on US Election News📰 | LSTM 🈚

Fake news classifier on US Election News📰 | LSTM 🈚

fake news classification featured Image

Introduction

Problem statement

Objective

Import Libraries

#Basic libraries
import pandas as pd 
import numpy as np 

#Visualization libraries
import matplotlib.pyplot as plt 
from matplotlib import rcParams
import seaborn as sns
from textblob import TextBlob
from plotly import tools
import plotly.graph_objs as go
from plotly.offline import iplot
%matplotlib inline
plt.rcParams['figure.figsize'] = [10, 5]
import cufflinks as cf
cf.go_offline()
cf.set_config_file(offline=False, world_readable=True)

#NLTK libraries
import nltk
import re
import string
from nltk.corpus import stopwords
from wordcloud import WordCloud,STOPWORDS
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
# Machine Learning libraries
import sklearn 
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import MultinomialNB 
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
 

#Metrics libraries
from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.model_selection import cross_val_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score

#Miscellanous libraries
from collections import Counter

#Ignore warnings
import warnings
warnings.filterwarnings('ignore')

#Deep learning libraries
from tensorflow.keras.layers import Embedding
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.text import one_hot
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Bidirectional
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout

Importing the dataset

#reading the fake and true datasets
fake_news = pd.read_csv('../input/fake-and-real-news-dataset/Fake.csv')
true_news = pd.read_csv('../input/fake-and-real-news-dataset/True.csv')

# print shape of fake dataset with rows and columns and information 
print ("The shape of the  data is (row, column):"+ str(fake_news.shape))
print (fake_news.info())
print("\n --------------------------------------- \n")

# print shape of true dataset with rows and columns and information
print ("The shape of the  data is (row, column):"+ str(true_news.shape))
print (true_news.info())

fake news classification details

Dataset Details

 

Preprocessing and Cleaning

Creating the target column

#Target variable for fake news
fake_news['output']=0

#Target variable for true news
true_news['output']=1

Concatenating title and text of news

#Concatenating and dropping for fake news
fake_news['news']=fake_news['title']+fake_news['text']
fake_news=fake_news.drop(['title', 'text'], axis=1)

#Concatenating and dropping for true news
true_news['news']=true_news['title']+true_news['text']
true_news=true_news.drop(['title', 'text'], axis=1)

#Rearranging the columns
fake_news = fake_news[['subject', 'date', 'news','output']]
true_news = true_news[['subject', 'date', 'news','output']]

Converting the date columns to datetime format

fake_news['date'].value_counts()

fake news classification value counts

#Removing links and the headline from the date column
fake_news=fake_news[~fake_news.date.str.contains("http")]
fake_news=fake_news[~fake_news.date.str.contains("HOST")]

'''You can also execute the below code to get the result 
which allows only string which has the months and rest are filtered'''
#fake_news=fake_news[fake_news.date.str.contains("Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec")]
#Converting the date to datetime format
fake_news['date'] = pd.to_datetime(fake_news['date'])
true_news['date'] = pd.to_datetime(true_news['date'])

Appending two datasets

frames = [fake_news, true_news]
news_dataset = pd.concat(frames)
news_dataset

Image for post

Text Processing

fake news classification text processing

News-Punctuation Cleaning

#Creating a copy 
clean_news=news_dataset.copy()def review_cleaning(text):
    '''Make text lowercase, remove text in square brackets,remove links,remove punctuation
    and remove words containing numbers.'''
    text = str(text).lower()
    text = re.sub('\[.*?\]', '', text)
    text = re.sub('https?://\S+|www\.\S+', '', text)
    text = re.sub('<.*?>+', '', text)
    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
    text = re.sub('\n', '', text)
    text = re.sub('\w*\d\w*', '', text)
    return textclean_news['news']=clean_news['news'].apply(lambda x:review_cleaning(x))
clean_news.head()

fake news classification clean news head

News-Stop words

stop = stopwords.words('english')
clean_news['news'] = clean_news['news'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))
clean_news.head()

fake news classification news stop words

Story Generation and Visualization from news

Count of the news subject

count of news subjects

Count of news subject based on true or fake

fake news classification count of new subjects

 

Count of fake news and true news

count of fake news and true news

 

Deriving new features from the news

fake news classification deriving new features from news

Polarity,Review length and wordcount

 

N-gram analysis

Top 20 words in News

fake news classification top 20 words in news

 

Top 2 words in the news

top 2 news in words

 

Top 3 words in the news

fake news classification top 20 trigrams

 

WordCloud of Fake and True News

Fake news

fake news classification fake news word cloud

True news

fake news classification true news word cloud

 

Time series analysis- Fake/True news

time series analysis fake news classification

 

Stemming the reviews

#Extracting 'reviews' for processing
news_features=clean_news.copy()
news_features=news_features[['news']].reset_index(drop=True)
news_features.head()

stemming the reviews

stop_words = set(stopwords.words("english"))
#Performing stemming on the review dataframe
ps = PorterStemmer()

#splitting and adding the stemmed words except stopwords
corpus = []
for i in range(0, len(news_features)):
    news = re.sub('[^a-zA-Z]', ' ', news_features['news'][i])
    news= news.lower()
    news = news.split()
    news = [ps.stem(word) for word in news if not word in stop_words]
    news = ' '.join(news)
    corpus.append(news)#Getting the target variable
y=clean_news['output']

Deep learning-LSTM

Deep learning-LSTM fake news classifier

One hot for Embedding layers

#Setting up vocabulary size
voc_size=10000

#One hot encoding 
onehot_repr=[one_hot(words,voc_size)for words in corpus]

Padding embedded documents

#Setting sentence length
sent_length=5000

#Padding the sentences
embedded_docs=pad_sequences(onehot_repr,padding='pre',maxlen=sent_length)
print(embedded_docs)

padding

LSTM Model

LSTM GRU

#Creating the lstm model
embedding_vector_features=40
model=Sequential()
model.add(Embedding(voc_size,embedding_vector_features,input_length=sent_length))
model.add(Dropout(0.3))
model.add(LSTM(100)) #Adding 100 lstm neurons in the layer
model.add(Dropout(0.3))
model.add(Dense(1,activation='sigmoid'))

#Compiling the model
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model.summary())

sequential model

Fitting the LSTM Model

# Converting the X and y as array
X_final=np.array(embedded_docs)
y_final=np.array(y)

#Check shape of X and y final
X_final.shape,y_final.shape

LSTM model

# Train test split of the X and y final
X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.33, random_state=42)

# Fitting with 10 epochs and 64 batch size
model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=64)

Last 4 epochs

4 epochs

Evaluation of model

# Predicting from test data
y_pred=model.predict_classes(X_test)

#Creating confusion matrix
#confusion_matrix(y_test,y_pred)
cm = metrics.confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cm,classes=['Fake','True'])

confusion matrix

#Checking for accuracy
accuracy_score(y_test,y_pred)

accuracy 1

# Creating classification report 
print(classification_report(y_test,y_pred))

classification

Bidirectional LSTM

Bidirectional LSTM

# Creating bidirectional lstm model
embedding_vector_features=40
model1=Sequential()
model1.add(Embedding(voc_size,embedding_vector_features,input_length=sent_length))
model1.add(Bidirectional(LSTM(100))) # Bidirectional LSTM layer
model1.add(Dropout(0.3))
model1.add(Dense(1,activation='sigmoid'))
model1.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model1.summary())

Image for post

Fitting and Evaluation of Model

# Fitting the model
model1.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=64)

fitting and evaluation model

# Predicting from test dataset
y_pred1=model1.predict_classes(X_test)

#Confusion matrix
cm = metrics.confusion_matrix(y_test, y_pred1)
plot_confusion_matrix(cm,classes=['Fake','True'])

confusion matrix

#Calculating Accuracy score
accuracy_score(y_test,y_pred1)

accuracy

# Creating classification report 
print(classification_report(y_test,y_pred1))

clssification

Conclusion

fake news conclusion

Author

Author

Ben Roshan

Ben is a Business Analytics student who loves to explore and thrive in the field of Data Science. He currently holds 2x Kaggle expert ranks. He looks forward to pursue his career as a Data Analyst.

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3 Comments

  • Ted says:

    Can you share the nature of your metadata and how your metadata segregate fake and true news?
    Thanks.

  • Clee says:

    Thanks for the posting.
    I have an error (KeyError: ‘title’) dwhen I run your code;

    #Concatenating and dropping for fake news
    fake_news[‘news’]=fake_news[‘title’]+fake_news[‘text’]
    fake_news=fake_news.drop([‘title’, ‘text’], axis=1)

    Have any idea of the source of this error?

  • soufiyan says:

    Thank you, very interesting