- ✕നിരവധി ഓൺലൈൻ സ്രോതസുകൾ അടിസ്ഥാനമാക്കി ആർട്ടിഫിഷ്യൽ ഇന്റലിജൻസ് ഉപയോഗിച്ച് സൃഷ്ടിച്ചതാണ് ഈ സംഗ്രഹം.
Fake news detection is a critical application of Natural Language Processing (NLP) and Machine Learning (ML) to combat misinformation. Below is a structured approach to building a fake news detection system using Python.
Steps to Build the Project
1. Dataset Preparation
Use datasets like the Fake and Real News Dataset from Kaggle.
The dataset typically contains columns like title, text, author, and label (1 for fake, 0 for real).
2. Data Preprocessing
Clean the text data: Remove stopwords, punctuation, and special characters. Convert text to lowercase. Tokenize and lemmatize words using libraries like nltk.
Example:
from nltk.corpus import stopwordsfrom nltk.stem import WordNetLemmatizerimport redef preprocess_text(text):lemmatizer = WordNetLemmatizer()stop_words = set(stopwords.words('english'))text = re.sub(r'[^a-zA-Z]', ' ', text).lower()tokens = [lemmatizer.lemmatize(word) for word in text.split() if word not in stop_words]return ' '.join(tokens)പകർത്തിയിരിക്കുന്നു!✕പകർത്തുക3. Feature Extraction
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