📌 Fake News Detection App

Developed with Python, Flask, and Machine Learning, this application offers:

🧠 Machine learning algorithms for news classification.

🗣️ Natural Language Processing for text analysis.

🌐 Web interface built with Flask.

⚡ Real-time news authenticity detection.

🔗 View the Project: Fake News Detection App


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🧠 NLP Interview Questions with Answers (Part 1)

1️⃣ What is Natural Language Processing (NLP)?

👉 NLP is a branch of AI that enables computers to understand, process, analyze, and generate human language.

Applications:

🔹 Chatbots 🤖
🔹 Machine Translation 🌐
🔹 Sentiment Analysis 😊
🔹 Text Summarization 📝
🔹 Speech Recognition 🎙️

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2️⃣ What is Tokenization in NLP?

👉 Tokenization is the process of breaking text into smaller units called tokens, such as words, subwords, or sentences.

Example:

text id="npl8x2"
"I love Machine Learning"
          ↓
["I", "love", "Machine", "Learning"]

💡 Tokenization is usually one of the first steps in NLP processing.

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3️⃣ What is Stop Word Removal?

👉 Stop words are common words that may carry relatively little useful information for certain NLP tasks.

Examples:

the, is, a, an, and, of, in

Example:

"The cat is on the table"
              ↓
"cat table"

💡 Stop-word removal is task-dependent and is not always appropriate, especially for modern language models.

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4️⃣ What is Stemming in NLP?

👉 Stemming reduces words to a simpler root-like form, usually by removing prefixes or suffixes.

Example:

playing
played
plays
   ↓
play

💡 Stemming is fast, but the resulting root may not always be a valid dictionary word.

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5️⃣ What is Lemmatization in NLP?

👉 Lemmatization converts a word into its base or dictionary form using linguistic information.

Example:

running → run
better  → good
studies → study

📌 Stemming → Rule-based word reduction
📌 Lemmatization → Linguistically informed base form

💡 Lemmatization generally produces more meaningful results than stemming, but can require more processing.

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💬 Save this for your NLP interview preparation!

🔥 Next Part will cover 5 important NLP questions on Bag of Words, TF-IDF, N-grams, Word Embeddings & Sentiment Analysis.

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