This Repo collects Notebooks for NLP

Notebook for natural language processing tasks.

text classification, text to emotions notebook 🎨

Jupyter notebooks and files for STAT 689 project.

Notebook criado exclusivamente para aprendizado e está sendo atualizado.

the notebook and generated texts created for the DAGPap22

Jupyterlab Notebook for AI model creation for classifying inappropriate messages

Notebook uses TensorFlow to finetune BERT for text classification task

Machine learning classification project using Python scripts and notebooks to classify the utility of random Github README files. ML implementation done using databricks notebook.

A notebook on review classification for movies featured on the imdb page

A colab notebook in R containing sentiment analysis on US Airline tweets

A notebook that contains the usage of almost machine learning algorithms for multiclass (4 classes) text analysis

🗨️ This repository contains a collection of notebooks and resources for various NLP tasks using different architectures and frameworks.

This repository contains a single notebook, notebook.ipynb, which analyzes the ability of three machine learning algoithms — Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine — to determine whether customer reviews of the Disneyland amusement park in California are positive or negative.

The notebook classifies reddit posts to its respective subreddit using different machine learning models and Compares the performance of these models. And finds the best model.

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.

The project utilizes Natural Language Processing (NLP) techniques to preprocess and analyze video transcripts, and employs a BERT + Bi-GRU model for text classification. The code is implemented in a Google Colab notebook.

SkimLit is a Natural Language Processing based model which includes different embeddings and architectures for processing the PubMed dataset. Links to the papers which include the dataset and the details of the architectures are mentioned in the Colab notebook

Coursework project for STINTSY with the task of classifying excerpts according to who authored them. The Jupyter Notebook contains the ML text classification pipeline as well as a comprehensive documentation of the methodology and experiments done to achieve the best results.

This repository contains a Python script for sentiment analysis of tweets using a Multinomial Naive Bayes classifier. The code demonstrates a complete pipeline, from data loading and text pre-processing to model training and evaluation. It includes a Jupyter Notebook (or Python script) that can be used to analyze sentiment in tweets.