textpreprocessing

A lightweight Python library that cleans messy scraped data (URLs, emojis, symbols, noise) and prepares it for NLP tasks like sentiment analysis.

Moved to Codeberg, this repo is just a (temporary) mirror -- Fizz buzz is not yet another implementation of Fizz buzz. Fizz buzz is a demo of an homogeneous and consistent development and documentation environment.

Content-based movie recommendation system using text vectorization, cosine similarity, and Streamlit.

A structured repository covering Natural Language Processing fundamentals, text preprocessing, feature extraction techniques, and hands-on NLP implementations using Python.

A deep learning NLP project that applies neural network techniques for sentiment classification, enabling automated understanding of opinions and emotions expressed in text data.

Complete NLP roadmap for beginners — 5 Google Colab notebooks covering Text Preprocessing, BoW, TF-IDF, Word2Vec, LSTM, and BERT. Every line of code explained. CampusX playlist companion.

For the text Mining course I carried out a project related to the analysis and classification of the reviews of the "UCI ML Drug Review" dataset (link: https://archive.ics.uci.edu/ml/datasets/Drug+Review+Dataset+%28Drugs.com%29). I learned to apply techniques such as bag of words, TF-IDF and build sentiment analysis models through the Bert and V…

Rule-based sentiment analysis on Shakespeare text using Python and NLP preprocessing techniques.

Implemented a Content-based recommender using CountVectorizer on 4.8K+ TMDB movies, leveraging 5K-dimensional sparse vectors & selecting Cosine similarity over Euclidean distance for NLP relevance.

End-to-end implementations of Natural Language Processing (NLP) projects and experiments, featuring sentiment analysis, text preprocessing, and machine learning models.

This notebook contains entire text preprocessing pipeline for NLP problems. The ready-to-use functions require NLTK and SKlearn package installations. It also contains some prominent text classification models.

This project implements an NLP-based emotion detection system classifying text into six emotions: joy, love, surprise, anger, fear, and sadness. Using a BiLSTM with embeddings and preprocessing, it provides interactive predictions with confidence and probability visualization via Streamlit.

LangChainRunnables is a Python-based project showcasing and learing five distinct LangChain workflows—Branch, Lambda, Parallel, Passthrough, and Sequence—using OpenRouter’s free API. It demonstrates AIdriven text processing tasks generating facts, summarizing reports, creating notes and quizzes, responding to sentiment feedback, and crafting jokes

An interactive Gradio app that demonstrates comprehensive text preprocessing techniques for Natural Language Processing (NLP). Visualize how each preprocessing step transforms your text with color-coded changes and explore tokenization, stemming, and lemmatization in real-time.

Chat gpt with hugging face

It is Persian Text processing with parsivar library.(University project)

A collection of modular and reusable machine learning utilities, tools, and helpers

📚 Performed sentiment analysis on Amazon Kindle reviews using BoW, TF-IDF & Word2Vec. 📊 Analyzed model performance and insights from review patterns.

This repo contains the Spam classification using NLP