01 — Problem
What was the objective?
The goal was to classify social-media text into positive, negative, and neutral sentiment for clearer understanding of text feedback.
ProjectNLP
Sentiment Analysis App
A three-class sentiment analysis application that classifies text into positive, negative, and neutral sentiment using NLP and machine learning.
Problem & Solution
From a defined problem to a practical solution.
02 — Solution
What was built?
Built a three-class NLP application that preprocesses text, extracts TF-IDF features, evaluates classification models, and delivers predictions through Streamlit.
Approach / Workflow
How the project moves from input to outcome.
01
Text Preprocessing
02
TF-IDF
03
Model Training
04
Evaluation
05
Streamlit Deployment
Tools & Technologies
Technologies used to build the project.
PythonScikit-learnTF-IDFNLPStreamlit
Key Work
What went into building the project.
01
Text preprocessing
02
Data preparation
03
TF-IDF feature extraction
04
Sentiment classification
05
Model evaluation
06
Prediction workflow
07
Streamlit deployment
Project Preview
A closer look at the application.
Project Links
Explore the source code and live application.
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