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---
title: Sentiment Analysis on Encrypted Data Using Fully Homomorphic Encryption
emoji: 🥷💬
colorFrom: yellow
colorTo: yellow
sdk: gradio
sdk_version: 3.2
app_file: app.py
pinned: true
tags: [FHE, PPML, privacy, privacy preserving machine learning, homomorphic encryption, security]
python_version: 3.10.11
---
# Sentiment Analysis With FHE
## Set up the app locally
- First, create a virtual env and activate it:
```bash
python3 -m venv .venv
source .venv/bin/activate
```
- Then, install required packages:
```bash
pip3 install pip --upgrade
pip3 install -U pip wheel setuptools --ignore-installed
pip3 install -r requirements.txt --ignore-installed
```
- (optional) Compile the FHE algorithm:
```bash
python3 compile.py
```
Check it finish well (with a "Done!"). Please note that the actual model initialization and training
can be found in the [SentimentClassification notebook](SentimentClassification.ipynb) (see below).
### Launch the app locally
- In a terminal:
```bash
source .venv/bin/activate
python3 app.py
```
## Interact with the application
Open the given URL link (search for a line like `Running on local URL: http://127.0.0.1:8888/` in the
terminal).
## Train a new model
The notebook [SentimentClassification notebook](SentimentClassification.ipynb) provides a way to
train a new model. Be aware that the data needs to be downloaded beforehand using the
[download_data.sh](download_data.sh) file (which requires Kaggle CLI).
Alternatively, the dataset can be downloaded manually at
https://www.kaggle.com/datasets/crowdflower/twitter-airline-sentiment
```bash
bash download_data.sh
```
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