Introduction to NLP — Practice Notebooks

This repository contains a collection of Jupyter notebooks created for practicing and exploring Natural Language Processing (NLP) concepts in Python.

The notebooks focus on hands-on experimentation with common NLP techniques, libraries, and workflows, including text preprocessing, tokenization, sentiment analysis, embeddings, and transformer-based models.

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Contents

• NLP fundamentals using Python
• Text preprocessing and normalization
• Tokenization and n-grams
• Sentiment analysis (rule-based and transformer-based)
• Word embeddings and vector representations
• Practical experiments using popular NLP libraries

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Environment & Dependencies

All notebooks were developed and tested inside a dedicated conda environment with the following package versions:

• python 3.11
• nltk 3.9.1
• pandas 2.2.3
• matplotlib 3.10.0
• spacy 3.8.3
• textblob 0.18.0.post0
• vaderSentiment 3.3.2
• transformers 4.47.1
• scikit-learn 1.6.0
• gensim 4.3.3
• seaborn 0.13.2
• torch 2.5.1
• ipywidgets 8.1.5

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Setup Instructions (Recommended)

Using a virtual environment helps avoid dependency conflicts and keeps the project self-contained.

Create and activate the environment

conda create --name nlp_env python=3.11
conda activate nlp_env