https://github.com/prem07a/twittersentiment
Analyzing Twitter sentiments with NLP deployed on Streamlit. Predicts sentiment (positive/negative) based on user-input text.
https://github.com/prem07a/twittersentiment
machine-learning nlp-machine-learning python3 sentiment-analysis twitter-sentiment-analysis
Last synced: about 1 year ago
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Analyzing Twitter sentiments with NLP deployed on Streamlit. Predicts sentiment (positive/negative) based on user-input text.
- Host: GitHub
- URL: https://github.com/prem07a/twittersentiment
- Owner: Prem07a
- License: mit
- Created: 2023-12-19T12:21:36.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-12T08:08:04.000Z (almost 2 years ago)
- Last Synced: 2025-02-05T14:46:17.152Z (over 1 year ago)
- Topics: machine-learning, nlp-machine-learning, python3, sentiment-analysis, twitter-sentiment-analysis
- Language: Python
- Homepage: https://twitter-sentiment-nlp.streamlit.app/
- Size: 49.1 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Twitter Sentiment Analysis using NLP and Streamlit
## Overview
This project implements a Twitter sentiment analysis tool using Natural Language Processing (NLP) techniques. The application is deployed using Streamlit, allowing users to interact with the sentiment analysis model through a user-friendly interface.
## Table of Contents
- [Overview](#overview)
- [Project Structure](#project-structure)
- [Setup](#setup)
- [Usage](#usage)
- [Demo](#demo)
- [License](#license)
## Project Structure
- **app.py**: Streamlit application script containing the main logic for the sentiment analysis tool.
- **models/**: Directory containing NLP model files or information.
- **requirements.txt**: File listing required dependencies for the project.
- **LICENSE**: License file for the project.
## Setup
To run the project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/Prem07a/TwitterSentiment.git
cd TwitterSentiment
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
Run the Streamlit app with the following command:
```bash
streamlit run app.py
```
Access the application in your web browser at `http://localhost:8501`. Interact with the sentiment analysis tool to analyze Twitter text for sentiment.
## Demo
#### 1. Negative Tweet
#### 2. Positive Tweet
## License
This project is licensed under the [MIT License](LICENSE).
```
@Prem Gaikwad 2023