Sentiment Analysis Using Classical Machine Learning

Live App: Streamlit Demo
Dataset: Kaggle Dataset
Demo Video: Watch on YouTube

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Project Overview

This project presents a unified sentiment analysis platform built using classical machine learning models. The primary goal is to classify textual data (e.g., reviews or comments) based on emotional tone — positive or negative.

Features

• Text preprocessing (cleaning, normalization)
• TF-IDF and Bag-of-Words (BoW) vectorization
• Dimensionality reduction using Singular Value Decomposition (SVD)
• Multiple ML algorithms:
  • Logistic Regression
  • Linear Regression
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Decision Tree
  • Artificial Neural Network (ANN)
  • Support Vector Machine (SVM)
  • Clustering (Unsupervised)
• Interactive real-time inference and model comparison via a Streamlit dashboard
• Modular and scalable architecture

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How to Use the Web App

1. Open the app:
   https://omchandrasharma-sentiment-analysis-sentiment-appapp-p762tr.streamlit.app/

2. In the left sidebar, select any model (e.g., Logistic Regression, KNN, Naive Bayes)

3. Scroll to the "Live Simulation" section

4. Enter a sentence or review in the input box

5. Click the "Predict" button

6. View the output label (Positive/Negative) and the prediction confidence score

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Folder Structure

. ├── ANN/ # Artificial Neural Network model, graphs & analysis ├── backend/ # FastAPI backend for model inference ├── clustering/ # Clustering algorithms and visualizations ├── decision_tree/ # Decision Tree implementation and analysis ├── KNN/ # K-Nearest Neighbors model and plots ├── linear_regression/ # Linear Regression model, graphs & performance ├── logistic_regression/ # Logistic Regression implementation & visuals ├── naive_bayes/ # Naive Bayes model for classification & sentiment ├── sentiment_app/ # Sentiment analysis logic and app code ├── svm/ # Support Vector Machine implementation and evaluation ├── .DS_Store ├── .gitattributes ├── README.md ├── Sentiment Analysis Dataset Report.pdf ├── SentimentClassificationReport.pdf

Installation

Install the required dependencies using pip:

pip install -r requirements.txt