Fake news on different platforms is spreading widely and is a matter of serious concern, as it causes social wars and permanent breakage of the bonds established among people. A lot of research is already going on focused on the classification of fake news.
Here we will try to solve this issue with the help of machine learning in Python.
Before starting the code, download the dataset by clicking the link.
Steps to be followed
- Importing Libraries and Datasets
- Data Preprocessing
- Preprocessing and analysis of News column
- Converting text into Vectors
- Model training, Evaluation, and Prediction
Importing Libraries and Datasets
The libraries used are :
- Pandas: For importing the dataset.
- Seaborn/Matplotlib: For data visualization.
Python3
import pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt |
Let’s import the downloaded dataset.
Python3
data = pd.read_csv('News.csv',index_col=0)data.head() |
Output :
Data preprocessing
The shape of the dataset can be found by the below code.
Python3
data.shape |
Output:
(44919, 5)
As the title, subject and date column will not going to be helpful in identification of the news. So, we can drop these column.
Python3
data = data.drop(["title", "subject","date"], axis = 1) |
Now, we have to check if there is any null value (we will drop those rows)
Python3
data.isnull().sum() |
Output:
text 0 class 0
So there is no null value.
Now we have to shuffle the dataset to prevent the model to get bias. After that we will reset the index and then drop it. Because index column is not useful to us.
Python3
# Shufflingdata = data.sample(frac=1)data.reset_index(inplace=True)data.drop(["index"], axis=1, inplace=True) |
Now Let’s explore the unique values in the each category using below code.
Python3
sns.countplot(data=data, x='class', order=data['class'].value_counts().index) |
Output:
Preprocessing and analysis of News column
Firstly we will remove all the stopwords, punctuations and any irrelevant spaces from the text. For that NLTK Library is required and some of it’s module need to be downloaded. So, for that run the below code.
Python3
from tqdm import tqdmimport reimport nltknltk.download('punkt')nltk.download('stopwords')from nltk.corpus import stopwordsfrom nltk.tokenize import word_tokenizefrom nltk.stem.porter import PorterStemmerfrom wordcloud import WordCloud |
Once we have all the required modules, we can create a function name preprocess text. This function will preprocess all the data given as input.
Python3
def preprocess_text(text_data): preprocessed_text = [] for sentence in tqdm(text_data): sentence = re.sub(r'[^\w\s]', '', sentence) preprocessed_text.append(' '.join(token.lower() for token in str(sentence).split() if token not in stopwords.words('english'))) return preprocessed_text |
To implement the function in all the news in the text column, run the below command.
Python3
preprocessed_review = preprocess_text(data['text'].values)data['text'] = preprocessed_review |
This command will take some time (as the dataset taken is very large).
Let’s visualize the WordCloud for fake and real news separately.
Python3
# Realconsolidated = ' '.join( word for word in data['text'][data['class'] == 1].astype(str))wordCloud = WordCloud(width=1600, height=800, random_state=21, max_font_size=110, collocations=False)plt.figure(figsize=(15, 10))plt.imshow(wordCloud.generate(consolidated), interpolation='bilinear')plt.axis('off')plt.show() |
Output :
Python3
# Fakeconsolidated = ' '.join( word for word in data['text'][data['class'] == 0].astype(str))wordCloud = WordCloud(width=1600, height=800, random_state=21, max_font_size=110, collocations=False)plt.figure(figsize=(15, 10))plt.imshow(wordCloud.generate(consolidated), interpolation='bilinear')plt.axis('off')plt.show() |
Output :
Now, Let’s plot the bargraph of the top 20 most frequent words.
Python3
from sklearn.feature_extraction.text import CountVectorizer def get_top_n_words(corpus, n=None): vec = CountVectorizer().fit(corpus) bag_of_words = vec.transform(corpus) sum_words = bag_of_words.sum(axis=0) words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()] words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True) return words_freq[:n] common_words = get_top_n_words(data['text'], 20)df1 = pd.DataFrame(common_words, columns=['Review', 'count']) df1.groupby('Review').sum()['count'].sort_values(ascending=False).plot( kind='bar', figsize=(10, 6), xlabel="Top Words", ylabel="Count", title="Bar Chart of Top Words Frequency") |
Output :
Converting text into Vectors
Before converting the data into vectors, split it into train and test.
Python3
from sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.linear_model import LogisticRegression x_train, x_test, y_train, y_test = train_test_split(data['text'], data['class'], test_size=0.25) |
Now we can convert the training data into vectors using TfidfVectorizer.
Python3
from sklearn.feature_extraction.text import TfidfVectorizer vectorization = TfidfVectorizer()x_train = vectorization.fit_transform(x_train)x_test = vectorization.transform(x_test) |
Model training, Evaluation, and Prediction
Now, the dataset is ready to train the model.
For training we will use Logistic Regression and evaluate the prediction accuracy using accuracy_score.
Python3
from sklearn.linear_model import LogisticRegression model = LogisticRegression()model.fit(x_train, y_train) # testing the modelprint(accuracy_score(y_train, model.predict(x_train)))print(accuracy_score(y_test, model.predict(x_test))) |
Output :
0.993766511324171 0.9893143365983972
Let’s train with Decision Tree Classifier.
Python3
from sklearn.tree import DecisionTreeClassifier model = DecisionTreeClassifier()model.fit(x_train, y_train) # testing the modelprint(accuracy_score(y_train, model.predict(x_train)))print(accuracy_score(y_test, model.predict(x_test))) |
Output :
0.9999703167205913 0.9951914514692787
The confusion matrix for Decision Tree Classifier can be implemented with the code below.
Python3
# Confusion matrix of Results from Decision Tree classificationfrom sklearn import metricscm = metrics.confusion_matrix(y_test, model.predict(x_test)) cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[False, True]) cm_display.plot()plt.show() |
Output :
Conclusion
Decision Tree Classifier and Logistic regression are performing well.
