Jupyter notebooks on Natural Language Processing.

demistifying nlp with a series of nlp implementation notebooks.

A collection of NLP related scripts and notebooks for using the framework flair (https://github.com/flairNLP/flair)

A comprehensive set of Jupyter notebooks that take you from NLP fundamentals to advanced techniques. Covers text preprocessing, POS tagging, NER, sentiment analysis (with VADER), text classification, word embeddings, and transformer models like BERT. Built with real-world datasets using NLTK, spaCy, scikit-learn, and Hugging Face Transformers.

This repository contains a Jupyter notebook implementing the Multinomial Naive Bayes algorithm from scratch for an email classification task of SPAM or HAM. The notebook also includes a comparison of the results obtained with the scikit-learn implementation of Multinomial Naive Bayes.

Short Description A sentiment analysis project for movie reviews 🎬 with a focus on NLP pre-processing. Solves a binary classification task in a Jupyter Notebook using NLTK and SpaCy.

Corpus building and NLP analysis for Persian Telegram channel messages. Includes a notebook to parse and clean channel_messages.json, stopword normalization, word cloud with a silhouette mask, and CSV outputs (filtered_messages.csv, final_results.csv). Reproducible pipeline for EDA and basic modeling.

The repository contains notebooks created for collecting and preprocessing the corpus of diary entries and for experiments on creating models for predicting gender, age groups of authors and the time period of text creation.