https://github.com/shallowmanica/covid19-sentiment-analysis

A deep learning project that classifies COVID-19 tweet sentiments using CNN, ANN, and LSTM models. The project uses the manually tagged "Coronavirus tweets NLP - Text Classification" dataset from Kaggle to analyze public sentiment during the pandemic.
https://github.com/shallowmanica/covid19-sentiment-analysis

cnn lstm nltk pytorch rnn sentiment-analysis sklearn tensorflow text-classification

Last synced: 3 months ago
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A deep learning project that classifies COVID-19 tweet sentiments using CNN, ANN, and LSTM models. The project uses the manually tagged "Coronavirus tweets NLP - Text Classification" dataset from Kaggle to analyze public sentiment during the pandemic.

• Host: GitHub
• URL: https://github.com/shallowmanica/covid19-sentiment-analysis
• Owner: shallowManica
• License: mit
• Created: 2025-03-28T20:21:31.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-28T21:23:08.000Z (over 1 year ago)
• Last Synced: 2025-03-28T21:32:28.807Z (over 1 year ago)
• Topics: cnn, lstm, nltk, pytorch, rnn, sentiment-analysis, sklearn, tensorflow, text-classification
• Language: Jupyter Notebook
• Homepage:
• Size: 6.95 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# COVID-19 Tweet Sentiment Analysis Using CNN, RNN and LSTM

## Overview

This repository contains a Jupyter Notebook that demonstrates sentiment analysis on COVID-19-related tweets using deep learning techniques. The goal is to classify tweets into sentiment categories (e.g., Negative, Positive, Other) by exploring and comparing three deep learning architectures:

- **Convolutional Neural Network (CNN)**

- **Artificial Neural Network (ANN)**

- **Long Short-Term Memory (LSTM) Network**

These models help reveal patterns in public sentiment during the COVID-19 pandemic.

## Dataset

The dataset is sourced from [Kaggle: Coronavirus tweets NLP - Text Classification](https://www.kaggle.com/datasets/datatattle/covid-19-nlp-text-classification). It comprises tweets related to the COVID-19 pandemic that have been manually tagged for sentiment. Key columns in the dataset include:

- **Location:** The origin of the tweet.

- **Tweet At:** Timestamp of when the tweet was posted.

- **Original Tweet:** The text content of the tweet.

- **Label:** The manually assigned sentiment (e.g., Negative, Positive, Other).

## Features

- **Data Preprocessing:** Clean and tokenize tweet texts to prepare for model input.

- **Modeling:** Implementation and comparative analysis of three models:

  - **CNN:** To capture local features from text sequences.

  - **ANN:** A baseline deep learning model for text classification.

  - **LSTM:** To capture sequential dependencies and context in tweets.

- **Evaluation & Visualization:** Analyze model performance using accuracy metrics and visualize sentiment distribution and trends.

## Requirements

- Python 3.x

- Jupyter Notebook or JupyterLab

Required Python packages:

- pandas

- numpy

- nltk

- tensorflow (or keras)

- matplotlib

- seaborn

## Installation & Setup

1. **Clone the Repository:**

   ```bash

   git clone https://github.com/YourUsername/COVID19-Sentiment-Analysis.git

   ```

2. **Navigate to the Project Directory:**

   ```bash

   cd COVID19-Sentiment-Analysis

   ```

## Usage

- **Launch Jupyter Notebook:**

  ```bash

  jupyter notebook

  ```

- Open the `sentiment_analy.ipynb` notebook and run the cells sequentially to preprocess the data, train the models, and visualize the results.

## File Structure

- `sentiment_analy.ipynb`: The main notebook with the complete sentiment analysis workflow.

- `data/`: Directory to store the COVID-19 tweets dataset (or instructions to download it from Kaggle).