https://github.com/jdonepud/nlp-sentimentclassification

https://github.com/jdonepud/nlp-sentimentclassification

data-preprocessing-and-augmentation data-preprocessing-and-cleaning deep-learning gpt2 natural-language-processing-nlp pytorch-implementation scikit-learn-python sentiment-analysis text-classification-python transformers-models wordcloud-visualization

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/jdonepud/nlp-sentimentclassification
• Owner: jdonepud
• Created: 2025-02-15T04:09:33.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-02-15T04:19:36.000Z (9 months ago)
• Last Synced: 2025-05-29T22:39:46.610Z (6 months ago)
• Topics: data-preprocessing-and-augmentation, data-preprocessing-and-cleaning, deep-learning, gpt2, natural-language-processing-nlp, pytorch-implementation, scikit-learn-python, sentiment-analysis, text-classification-python, transformers-models, wordcloud-visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 747 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **NLP-Sentiment-Classification**

**Natural Language Processing (NLP) project** for **sentiment analysis** using machine learning and deep learning models.

## **Overview**

This project performs **sentiment classification on textual data** using **TF-IDF + traditional ML models (Naïve Bayes, RandomForest)** and **deep learning transformers (DistilBERT, GPT-2)**.

It includes:

- **Data Preprocessing**;

- **Exploratory Data Analysis (EDA)**;

-  **DistilBERT**;

-  **GPT-2 Text Generation**

-  **Machine Learning Models**;

-  **Deep Learning Models**;

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## **Features**

--> **Sentiment Analysis** using classical ML and Transformer models  

--> **Data Preprocessing & Cleaning** (TF-IDF, stopword removal, lemmatization)  

--> **EDA with WordCloud & Histograms**  

--> **Model Evaluation** with Accuracy, Precision, Recall, F1-score  

--> **GPT-2 based text generation based on sentiment context**  

--> **Automated Training and Evaluation using Hugging Face Transformers**