Complete-Text-Analysis-Streamlit-Web-App

URL: https://github.com/BhaswatiRoy/Complete-Text-Analysis-Streamlit-Web-App

Description: A Text Analysis Web App that provides a detailed analysis of text input. The app supports five main types of analysis:

1. Spam or Ham Detection
2. Sentiment Analysis
3. Stress Detection
4. Hate & Offensive Content Detection
5. Sarcasm Detection

Project Overview

The web application is built using Streamlit and deployed on Streamlit Share. It utilizes machine learning models for text classification in different domains, including sarcasm detection.

Training Methods:

• Each prediction page is connected to a Machine Learning Model.
• Algorithms used:
  • Logistic Regression
  • Decision Tree
  • Random Forest
• Text preprocessing includes TF-IDF vectorization before feeding the text into the models.
• The model is trained to classify text based on its relevant category.

Results:

The repository does not provide explicit performance metrics or evaluation results for the sarcasm detection model.

Dataset Files:

• Sarcasm Detection.csv - size: 295 KB (used for sarcasm detection)

Deployment:

• The app is deployed using Streamlit and accessible via:
  https://share.streamlit.io/bhaswatiroy/complete-text-analysis-streamlit-web-app/main/app.py