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?

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)