Low accuracy for fake news detection model

[shivani]

I am trying to implement a model for fake news detection. The dataset I am using has been taken from this source :
https://drive.google.com/file/d/1er9NJTL...4a-_q/view

I am getting around 82% accuracy which is low compared to the other people models. Is there a better way to improve the accuracy of my model?

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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.metrics import accuracy_score, confusion_matrix   df=pd.read_csv('news.csv') print(df.head())   labels=df.label features = df['text']   x_train,x_test,y_train,y_test=train_test_split(features , labels, test_size=0.2, random_state=7)   tfidf_vectorizer=TfidfVectorizer(stop_words="english",max_df=0.7, analyzer='word',sublinear_tf = True)   tfidf_train=tfidf_vectorizer.fit_transform(x_train) tfidf_test=tfidf_vectorizer.transform(x_test)   tfidf_test.shape   from sklearn.naive_bayes import MultinomialNB clf = MultinomialNB().fit(tfidf_train, y_train)   y_pred=clf.predict(tfidf_test) score=accuracy_score(y_test,y_pred) print(f'Accuracy: {round(score*100,2)}%')   confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])

Output :

Output:
Accuracy: 82.24%
 
array([[419, 219],
       [  6, 623]], dtype=int64)

[animeshagrawal2807]

When it comes to accuracy you have to try different methods and tune the parameters to see what works better. In this dataset, the Multinomial Naive Bayes classifier does not perform well. After trying different models, Passive-aggressive classifier gave 93% accuracy.
The implementation of Passive aggressive classifier is given below:

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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.metrics import accuracy_score, confusion_matrix from sklearn.linear_model import PassiveAggressiveClassifier   df=pd.read_csv('news.csv') #print(df.head())   labels=df.label features = df['text']   x_train,x_test,y_train,y_test=train_test_split(features , labels, test_size=0.2, random_state=7)   tfidf_vectorizer=TfidfVectorizer(stop_words="english",max_df=0.7, analyzer='word',sublinear_tf = True)   tfidf_train=tfidf_vectorizer.fit_transform(x_train) tfidf_test=tfidf_vectorizer.transform(x_test)   pac=PassiveAggressiveClassifier(max_iter=50) pac.fit(tfidf_train,y_train)   y_pred=pac.predict(tfidf_test) score=accuracy_score(y_test,y_pred) print(f'Accuracy: {round(score*100,2)}%')   confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])

Output:
Accuracy: 93.37%
 
array([[594,  44],
       [ 40, 589]], dtype=int64)