📰 Fake News Detection & NLP Analysis

This project performs natural language processing (NLP) tasks and classification on a dataset of news articles to distinguish between Fake News and Factual News. It involves preprocessing, tokenization, Named Entity Recognition (NER), sentiment analysis, topic modeling, and machine learning classification.

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📁 Dataset

• fake_news_data.csv: Contains 198 news articles labeled as either "Fake News" or "Factual News".

Dataset Columns:

• title: Headline of the news article.
• text: Body of the news article.
• date: Publication date.
• fake_or_factual: Label ("Fake News" or "Factual News").

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🔧 Libraries Used

• pandas, matplotlib, seaborn
• spacy, nltk, re
• vaderSentiment
• gensim
• sklearn

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🧹 Preprocessing & Feature Engineering

• Lowercasing, punctuation removal, stopword filtering
• Tokenization using nltk
• Lemmatization using WordNetLemmatizer
• Named Entity Recognition with spaCy
• Sentiment scoring with VADER
• Bag of Words and TF-IDF features

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📊 Exploratory Data Analysis

• Distribution of fake vs factual news
• Part-of-speech tagging frequency
• Common named entities in each category
• Sentiment analysis across news types
• Top unigrams after preprocessing

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🧠 Topic Modeling

• LDA (Latent Dirichlet Allocation)
• LSA (Latent Semantic Analysis)
• Visualization of coherence scores for optimal topic number

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🤖 Machine Learning Models

Two models were trained using Bag of Words features:

Logistic Regression

• Accuracy: 90%
• Precision/Recall:
  • Fake News: 93% / 86%
  • Factual News: 88% / 94%

SGDClassifier (Linear SVM)

• Accuracy: 83%
• Precision/Recall:
  • Fake News: 91% / 72%
  • Factual News: 78% / 94%

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📈 Visualizations

• Count plots
• POS and NER distribution bars
• Sentiment bar charts
• LDA/LSA topic charts

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🚀 How to Run

1. Clone the repository.
2. Make sure fake_news_data.csv is in the root directory.
3. Install the dependencies:

pip install -r requirements.txt

4. Run the analysis in a Jupyter Notebook or Python script.

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🧾 Author

Vishnu M
LinkedIn: linkedin.com/in/vishnu-m737