Log In

Don't have an account? Sign up now

Lost Password?

Sign Up

Prev Next
Data Science > Natural Language Processing (NLP)

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of Artificial Intelligence that enables machines to read, understand, interpret, and generate human language.

Human language is:

  • Ambiguous
  • Context-dependent
  • Full of grammar rules and exceptions

NLP bridges the gap between human communication and machine understanding.

Why NLP Is Important

Almost 80% of the world’s data is unstructured text:

  • Emails
  • Chat messages
  • Reviews
  • Social media posts
  • News articles
  • Legal documents

NLP is used in:

  • Search engines
  • Chatbots and virtual assistants
  • Recommendation systems
  • Spam detection
  • Sentiment analysis
  • Resume screening
  • Machine translation

1. Text Preprocessing

What Is Text Preprocessing?

Text preprocessing is the process of cleaning and preparing raw text data so that it can be effectively used by machine learning or deep learning models.

Raw text is messy:

  • Uppercase and lowercase differences
  • Punctuation
  • Special characters
  • Spelling variations

Preprocessing converts raw text into a structured and consistent format.

Common Text Preprocessing Steps

  • Lowercasing text
  • Removing punctuation
  • Removing numbers
  • Removing special characters
  • Removing extra spaces
  • Handling emojis and URLs

Example

Raw text:

“This product is AMAZING!!! 😍🔥”

Processed text:

“this product is amazing”

2. Tokenization

What Is Tokenization?

Tokenization is the process of splitting text into smaller units called tokens.

Tokens can be:

  • Words
  • Characters
  • Subwords
  • Sentences

Why Tokenization Is Important

  • ML models cannot process raw text directly
  • Tokens act as the basic building blocks of NLP tasks

Types of Tokenization

Word Tokenization

Splits text into words.

Example:

“I love data science”

Tokens:

[“I”, “love”, “data”, “science”]

Sentence Tokenization

Splits text into sentences.

Example:

“Hello! How are you?”

Sentences:

[“Hello!”, “How are you?”]

Subword Tokenization

Breaks words into smaller meaningful units.
Used in modern NLP models like BERT and GPT.

3. Stop Words

What Are Stop Words?

Stop words are common words that appear frequently but carry little meaningful information.

Examples:

  • is
  • the
  • and
  • in
  • of

Why Remove Stop Words?

  • Reduces noise
  • Improves efficiency
  • Focuses on important words

When NOT to Remove Stop Words

  • Sentiment analysis
  • Question answering
  • Language understanding tasks

Example:

“This movie is not good”

Removing stop words incorrectly may remove “not”, changing meaning.

4. Stemming & Lemmatization

Why We Normalize Words

Words appear in different forms:

  • Play, playing, played
  • Run, running, ran

Normalization reduces them to a common base form.

Stemming

Stemming removes word suffixes using rule-based heuristics.

Example:

  • Playing → Play
  • Studies → Studi

Advantages:

  • Fast
  • Simple

Disadvantages:

  • Can produce incorrect words

Lemmatization

Lemmatization converts words to their dictionary form (lemma).

Example:

  • Running → Run
  • Better → Good

Advantages:

  • Linguistically correct
  • More meaningful

Disadvantages:

  • Slower
  • Requires language knowledge

5. Bag of Words (BoW)

What Is Bag of Words?

Bag of Words is a text representation technique that converts text into numerical vectors based on word frequency.

Order of words is ignored.

How Bag of Words Works

  1. Build vocabulary
  2. Count word occurrences
  3. Create feature vectors

Example

Sentences:

  • “I love NLP”
  • “I love AI”

Vocabulary:

[I, love, NLP, AI]

Vectors:

  • [1, 1, 1, 0]
  • [1, 1, 0, 1]

Limitations

  • Ignores word order
  • No semantic meaning
  • High dimensionality

6. TF-IDF

What Is TF-IDF?

TF-IDF (Term Frequency–Inverse Document Frequency) improves BoW by weighting important words more heavily.

TF (Term Frequency)

Measures how often a word appears in a document.

IDF (Inverse Document Frequency)

Measures how rare a word is across all documents.

Rare words get higher weight.

Why TF-IDF Is Better Than BoW

  • Reduces impact of common words
  • Highlights important keywords
  • Improves classification performance

Example

Word “data” in a data science article gets high TF-IDF score.

7. Word Embeddings

What Are Word Embeddings?

Word embeddings represent words as dense numerical vectors that capture semantic meaning.

Words with similar meanings have similar vectors.

Popular Word Embedding Techniques

  • Word2Vec
  • GloVe
  • FastText

Why Word Embeddings Are Powerful

  • Capture context
  • Preserve semantic relationships
  • Reduce dimensionality

Example

Vector arithmetic:

King – Man + Woman ≈ Queen

Modern Embeddings

  • BERT
  • GPT
  • Transformer-based embeddings

8. Text Classification

What Is Text Classification?

Text classification assigns predefined categories or labels to text.

Examples

  • Spam vs Not Spam
  • News topic classification
  • Resume screening
  • Intent detection

How Text Classification Works

  1. Text preprocessing
  2. Feature extraction
  3. Model training
  4. Prediction

Algorithms Used

  • Naive Bayes
  • Logistic Regression
  • SVM
  • Deep Learning (CNN, RNN, Transformers)

9. Sentiment Analysis

What Is Sentiment Analysis?

Sentiment analysis identifies the emotional tone behind text.

Types of Sentiment

  • Positive
  • Negative
  • Neutral

Advanced sentiment analysis detects:

  • Emotion
  • Sarcasm
  • Intensity

Real-World Applications

  • Product reviews
  • Social media monitoring
  • Brand reputation
  • Customer feedback analysis

Example

Text:

“The service was slow, but the food was excellent.”

Sentiment:

Mixed sentiment

Leave a Comment