Abhishek-Mallick / Abhishek-Mallick/universal-box

[TEMPLATE] - Sentiment Analysis - DataScience

Abierto
#148 2 comentarios 0 reacciones 0 asignados Ver en GitHub
good first issue hacktoberfest
Lenguaje dominante
JavaScript
Estrellas
47
Forks
41
Métricas de merge de PR
Sin PR fusionados en 30 d

Descripción

### What domain will the project template be based on?

Data Science

### If 'Others', please specify the preferred domain.

_No response_

### Particular directory path for the template.

`templates/Data-Science/NLP/Sentiment-Analysis/`

### What tech stacks do you want to include?

Python, NLTK, TextBlob

### Brief Description

A template for performing sentiment analysis on social media posts or product reviews. The project will utilize NLP techniques to classify sentiments as positive, negative, or neutral.

Note : A Jupyter notebook in form of a collab notebook should be linked in the README.md, and a user-friendly UI should be built using Flask or Streamlit.

### 👀 Have you spent some time checking if this issue has been raised before?

- [X] Yes
- [ ] No

### 🏢 Have you read the Code of Conduct?

- [X] I have read the [Code of Conduct](https://github.com/Abhishek-Mallick/universal-box/blob/main/.github/CODE_OF_CONDUCT.md)

### Would you like to work on this issue?

No

Guía de contribución

Abrir la guía de contribución

Línea de trabajo

Create a project template under templates/Data-Science/NLP/Sentiment-Analysis/. Include a Jupyter notebook for analysis, a Flask or Streamlit UI, and a README linking to the notebook. Check existing templates in the repository for structure and required files. Ensure the template demonstrates sentiment classification using NLTK and TextBlob on sample text data.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
flask, jupyter-notebook, python, streamlit
Área
ai, data, documentation, machine-learning, web-dev
Tipo de issue
Documentación
Dificultad
3/5
Tiempo estimado
1-2 días
Estado de actividad
Estancado
Claridad
Bien especificado
Aptitud para principiantes
65/100

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