Complete Text Analysis Web App 💬 📝 ✍️

This app is used to perform an indepth analysis of a text The analysis sections include ->

1. Spam or Ham Detection

2. Sentiment Analysis

3. Stress Detection

4. Hate & Offensive Content Detection

5. Sarcasm Detection

Tech Stacks Used

Libraries Used

Structure Of The Project

• Each prediction page is conneceted with a Machine Learning Model which uses either of Logistic Regression, Decision Tree, Random Forest Algorithms to predict the results.
• Also we have 5 different datasets being used for each prediction.
• We can land into each prediction site of the web app from the options in the Navigation Menu.
• We have only 1 relevant feature taken into consideration which is the text and then the text is preprocessed and vectoized with help of TF-IDF Vectorizer to fit into the model and tain it.
• So the user gets a broad overview of the text after the analysis

The feature taken into consideration

Text Analysis Type	Feature
Spam or Ham Detection Page	Text
Sentiment Analysis Page	Text
Stress Detection Page	Text
Hate & Offensive Content Page	Text
Sarcasm Detection	Text

The text is preprocessed then fed to the model.

Deployment Of The Project

After the modeling part the model is deployed using Streamlit library on Streamlit Share so that the app is available for usage for everyone.

Link To My Web Application -

https://share.streamlit.io/bhaswatiroy/complete-text-analysis-streamlit-web-app/main/app.py

Glance At The Hosted Application-

1. Home Page

2. Spam or Ham Detection Page

3. Sentiment Analysis Page

4. Stress Detection Page

5. Hate & Offensive Content Page

6. Sarcasm Detection Page