https://github.com/trflorian/sentiment-analysis-viz

Real-time visualization of sentiment analysis on text input
https://github.com/trflorian/sentiment-analysis-viz

customtkinter data-science huggingface opencv-python python sentiment-analysis tokenizer transformers

Last synced: 7 months ago
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Real-time visualization of sentiment analysis on text input

• Host: GitHub
• URL: https://github.com/trflorian/sentiment-analysis-viz
• Owner: trflorian
• License: mit
• Created: 2025-01-26T16:56:44.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-20T22:06:56.000Z (9 months ago)
• Last Synced: 2025-05-20T22:51:41.833Z (9 months ago)
• Topics: customtkinter, data-science, huggingface, opencv-python, python, sentiment-analysis, tokenizer, transformers
• Language: Python
• Homepage: https://towardsdatascience.com/real-time-interactive-sentiment-analysis-in-python/
• Size: 237 KB
• Stars: 18
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          



 


SENTIMENT ANALYSIS

![Python](https://img.shields.io/badge/python-3.13-blue.svg)

![GitHub License](https://img.shields.io/github/license/trflorian/sentiment-analysis-viz)



![demo](https://github.com/user-attachments/assets/baf39a9b-2025-4bd6-9ffe-79cc1ce992dd)



This project showcases a real-time sentiment analysis visualization with a smiley based on a text input.

The more positive the sentiment of the input is, the happier the smiley looks and vice versa if the sentiment is really negative.

## How it works

The text input is passed to a sentiment analysis pipeline with a tokenizer and a transformer model pre-trained on twitter data, roughly 58 million tweets.

The specific model used is `cardiffnlp/twitter-roberta-base-sentiment` (https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment), which outputs three labels and a confidence score each.

The labels correspond to the classifications *negative*, *neutral* and *positive*. To calculate the final sentiment score in a range of `[-1, 1]`, I simply apply a weighted scaling based on the confidence of each label. 

The negative label prediction confidence score is multiplied by -1, the neutral one by 0 and the positive score by 1.

## Procedural Smiley

Using linear color interpolation in the HSV color space and a simple parabola for the mouth of the smiley, the face is procedurally generated.

![score_smiley_sketch](https://github.com/user-attachments/assets/4e2dd697-fc68-40c6-b4c1-c67f9661f9e4)

## Examples

| Positive Sentiment | Negative Sentiment |

| -------- | ------- |

| ![love_is_in_the_air](https://github.com/user-attachments/assets/196b33d0-de47-4f3b-aaad-7d816b622184)  | ![bad weather](https://github.com/user-attachments/assets/d55f4598-065c-4f98-8814-dcd8452a35d5)    |

## Quickstart

### Prerequisites

This project uses [uv](https://docs.astral.sh/uv/) as its package/project manager.

You can run `uv sync` in the cloned project folder to get all dependencies setup in a local virtual environment specific for this project.

### Run Demo

To run the project there are currently two applications available:

- An interactve web app using Marimo

- A desktop application using TKinter

#### Interactive Web App

The project includes an interactive web application built with Marimo that provides a user-friendly interface on a webpage. 

```bash

uv run marimo run src/sentiment_analysis/marimo/marimo_app.py

```

![marimo_app](https://github.com/user-attachments/assets/f8b92b65-d26c-4f81-88c3-667a83be720e)

#### Tkinter Desktop App

Make sure that you have all required packages installed for tkinter in order to run the standalone application.

```bash

uv run ctk_app

```

![ctk](https://github.com/user-attachments/assets/379c2e76-c9c0-4187-b70e-e771dd9487ef)