We’ve cleaned and structured our text, but understanding language isn’t just about removing noise—it’s also about analyzing meaning, context, and structure.

Now, let’s explore Linguistic Analysis, where text transforms from raw words into structured, machine-readable knowledge.

1️⃣ Part-of-Speech (POS) Tagging: Understanding Sentence Structure

📌 Problem:

Words can have multiple meanings based on context. For example:

• "Book a flight" (verb)
• "Read a book" (noun)

If an NLP model doesn’t recognize word roles, it might misinterpret sentences.

💡 Solution: POS tagging assigns labels (noun, verb, adjective, etc.) to words.

import nltk
nltk.download('averaged_perceptron_tagger')

text = "The quick brown fox jumps over the lazy dog"
tokens = nltk.word_tokenize(text)
pos_tags = nltk.pos_tag(tokens)

print(pos_tags)

🔹 Output:

[('The', 'DT'), ('quick', 'JJ'), ('brown', 'JJ'), ('fox', 'NN'), ('jumps', 'VBZ'), ('over', 'IN'), ('the', 'DT'), ('lazy', 'JJ'), ('dog', 'NN')]

👉 Why is this useful?

• Helps NLP models understand sentence structure.
• Improves text-to-speech, machine translation, and chatbot responses.

2️⃣ Named Entity Recognition (NER): Extracting Important Information

📌 Problem:

Not all words are equal—some represent specific names, locations, organizations, or dates. Consider:

• "Apple released a new iPhone in California on September 12."
  • Apple (Company)
  • iPhone (Product)
  • California (Location)
  • September 12 (Date)

💡 Solution: NER extracts meaningful entities from text.

import spacy
nlp = spacy.load("en_core_web_sm")

text = "Apple Inc. was founded by Steve Jobs in California."
doc = nlp(text)

for ent in doc.ents:
    print(ent.text, ent.label_)

🔹 Output:

Apple Inc. ORG  
Steve Jobs PERSON  
California GPE  

👉 Why is this useful?

• Essential for chatbots, search engines, and recommendation systems.
• Used in resume parsing, financial analysis, and fraud detection.

3️⃣ Vectorization: Converting Text into Numbers

📌 Problem:

Machines don’t understand words—they need numbers to process information.

💡 Solution: Convert text into numeric vectors using techniques like:

• Bag of Words (BoW): Counts word occurrences.
• TF-IDF: Weighs important words.
• Word Embeddings (Word2Vec, BERT): Captures meaning and context.

Example: TF-IDF Vectorization

from sklearn.feature_extraction.text import TfidfVectorizer

documents = ["Machine learning is amazing", "Deep learning is a subset of machine learning"]
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(documents)

print(vectorizer.get_feature_names_out())
print(vectors.toarray())

🔹 Output: (Feature words and their weights)

['amazing' 'deep' 'learning' 'machine' 'of' 'subset' 'is']
[[0.707 0.    0.5    0.5    0.    0.    0.5  ]
 [0.    0.5    0.5    0.5    0.5    0.5    0.5  ]]

👉 Why is this useful?

• Enables text classification, sentiment analysis, and machine translation.
• Helps NLP models compare and process words efficiently.

4️⃣ Handling Missing Data: Dealing with Incomplete Text

📌 Problem:

In real-world data, some text fields might be empty or corrupted.

💡 Solution:

• Remove missing values if the data is irrelevant.
• Fill missing text using techniques like imputation.

import pandas as pd
data = pd.DataFrame({"text": ["NLP is powerful", None, "Machine learning is fun", ""]})

# Fill missing values with "Unknown"
data['text'].fillna("Unknown", inplace=True)
data['text'].replace("", "Unknown", inplace=True)

print(data)

🔹 Output:

                    text
0       NLP is powerful
1                Unknown
2  Machine learning is fun
3                Unknown

👉 Why is this useful?

• Prevents errors in machine learning models.
• Ensures data consistency in NLP pipelines.

5️⃣ Normalization: Standardizing Text for Consistency

📌 Problem:

Different variations of words can confuse NLP models:

• "U.S.A." vs. "USA"
• "5km" vs. "5 kilometers"
• "₹500" vs. "$6.50"

💡 Solution: Convert text to a uniform standard.

text = "The price is ₹500 or $6.50."
text = text.replace("₹", "Rs.")  # Convert currency symbols
text = text.replace("$", "USD ")

print(text)

🔹 Output:

The price is Rs.500 or USD 6.50.

👉 Why is this useful?

• Improves consistency in text analysis.
• Helps search engines and AI models process data efficiently.

6️⃣ Spelling Correction: Fixing Typos for Better Accuracy

📌 Problem:

Typos and misspellings can mislead NLP models:

• "I recived an invitaton" → ❌
• "I received an invitation" → ✅

💡 Solution: Use spelling correction techniques like TextBlob or Hunspell.

from textblob import TextBlob

text = "I recived an invitaton to the event."
corrected_text = str(TextBlob(text).correct())

print(corrected_text)

🔹 Output:

I received an invitation to the event.

👉 Why is this useful?

• Improves search engines, chatbots, and document processing.
• Enhances data quality for sentiment analysis and topic modeling.

🚀 Wrapping Up: The Power of Linguistic Analysis

We’ve transformed raw text into structured, machine-friendly data! 🎯

✅ POS Tagging → Understands word roles (noun, verb, etc.).

✅ NER → Extracts important entities (names, locations, organizations).

✅ Vectorization → Converts text into numeric form for ML models.

✅ Handling Missing Data → Ensures data consistency.

✅ Normalization → Standardizes text for uniformity.

✅ Spelling Correction → Fixes typos for accuracy.

📌 Next Up: Deep NLP—Sentence Parsing, Syntax Trees, and Dependency Analysis! 🚀