https://github.com/rijoslal/sentio
Sentio is a sentiment & toxicity analysis web app built with LSTM using TensorFlow and Streamlit. It classifies text comments as Positive, Negative, or Neutral and detects Toxic content in the comments, perfect for analyzing reviews, social media posts, or feedback
https://github.com/rijoslal/sentio
lstm-neural-networks sklearn streamlit tensorflow
Last synced: 11 months ago
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Sentio is a sentiment & toxicity analysis web app built with LSTM using TensorFlow and Streamlit. It classifies text comments as Positive, Negative, or Neutral and detects Toxic content in the comments, perfect for analyzing reviews, social media posts, or feedback
- Host: GitHub
- URL: https://github.com/rijoslal/sentio
- Owner: RijoSLal
- Created: 2024-12-13T14:43:59.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-13T06:12:48.000Z (over 1 year ago)
- Last Synced: 2025-04-07T06:51:21.499Z (about 1 year ago)
- Topics: lstm-neural-networks, sklearn, streamlit, tensorflow
- Language: Jupyter Notebook
- Homepage: https://sentio.streamlit.app/
- Size: 63.3 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sentio - Sentiment & Toxicity Analysis Web App
**Sentio** is a sentiment analysis web application based on LSTM built using TensorFlow, Streamlit and other essential libraries. It allows users to classify text comments as **Positive**, **Negative**, or **Neutral** and classify **Toxicity** contents in the comment by simply entering text into a text field.
## Features
- Classifies comments into **Positive**, **Negative**, or **Neutral** categories.
- Identify **Toxic** contents in comments
- Provides a user-friendly interface using Streamlit.
- Uses a LSTM model for sentiment classification.
- Visualizes the sentiment prediction and toxicity with color-coded progress bars.
## Tech Stack
- **Streamlit**: For building the web interface.
- **TensorFlow**: For deep learning and sentiment classification.
- **Keras**: For implementing the neural network model.
- **Pandas**: For data manipulation.
- **NumPy**: For numerical computations.
- **Pickle**: For saving and loading the tokenizer model.
- **sklearn**: For splitting data and preprocessing
## Requirements
To install the required packages, create a virtual environment and install the dependencies from `requirements.txt`:
```bash
pip install -r requirements.txt
```
The `requirements.txt` file should include:
```
streamlit
pandas
numpy
tensorflow-cpu
pickle-mixin
sklearn
```
## How to Run the App
1. Clone the repository to your local machine:
```bash
git clone https://github.com/RijoSLal/sentio.git
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Streamlit app:
```bash
streamlit run sentio.py
```
4. Open your browser and go to `http://localhost:8501` to use the Sentio app.
## Files
- **sentio.py**: The Streamlit app for the sentiment analysis interface.
- **sentiment.h5**: The trained sentiment analysis model.
- **tokenizer.pkl**: The tokenizer used to preprocess input text.
- **train.csv**: The training dataset.
- **test.csv**: The test dataset.
- **toxic_data.csv**: the dataset for toxicity detection
- **sentiment_toxic.h5**: The trained toxicity recognition model.
- **tokenizer_toxic.pkl**: The tokenizer used to preprocess toxic text.
## License
This project is licensed under the MIT License. Feel free to modify, use, and distribute the code in any way you like. See the LICENSE file for details.