Working with Text in NLP
Nlp tutorial · PySpark.in
Working with Text
Text data is any information written in natural language.
Common examples:
- Messages and emails
- Reviews and comments
- Articles and documents
- Chat conversations
For humans, text has meaning automatically.
For machines, text is just a sequence of characters.
How Text Looks to a Machine
When a machine receives text, it does not understand:
- Meaning
- Emotion
- Context
It only sees:
- Letters
- Spaces
- Symbols
Working with Text in Python
In Python, text is handled using strings.
Example: Creating Text Data
```
text = "Natural Language Processing is interesting"
print(text)
```
1. Basic Text Cleaning
Real-world text is messy.
Before analysis, we usually clean it.
Common first steps:
- Convert to lowercase
- Remove punctuation
- Remove extra spaces
1. Lowercasing Text
```
text = "NLP Is Powerful"
clean_text = text.lower()
print(clean_text)
```
Lowercasing helps treat words like “NLP” and “nlp” as the same.
2. Breaking Text into Words (Tokenization)
Tokenization means splitting text into smaller units, usually words.
This is one of the most important steps in NLP.
```
text = "NLP makes machines understand language"
tokens = text.split()
print(tokens)
```
Now the machine can work with individual words instead of a long sentence.
Why These Steps Matter
Even simple steps like:
- cleaning text
- splitting words
help machines:
- detect patterns
- compare text
- prepare for learning models later
Without this step, advanced NLP models cannot work properly.
Installing NLP Libraries
```
hide
pip install nltk
pip install spacy
pip install scikit-learn
pip install transformers
```
These libraries provide tools for:
- tokenization
- linguistic analysis
- machine learning
- modern NLP models
What You Learned in This Chapter
- What text data looks like from a machine’s perspective
- How raw text is represented and handled in Python
- Why cleaning text is a necessary first step
- How sentences are broken into individual words using tokenization
- Why these simple steps form the foundation of every NLP system
Understanding these basics makes it easier to move forward into deeper language analysis and more advanced NLP techniques.
More Nlp tutorials
- Natural Language Understanding (NLU) , Natural Language Generation (NLG) and phases of NL
- Tokenization in NLP and NLP Project Life Cycle
- Coverting The Text to Vector(one hot encoding and bag of words method)
- Convert text to vector: N-grams and TF-IDF method
- Word Embedding
- What is Natural Language Processing ?
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