🧠 Sentiment Analysis from Scratch (No ML Libraries)

This project implements a simple sentiment analysis classifier using logistic regression, trained using manual gradient descent, without relying on any machine learning libraries like scikit-learn or TensorFlow. It demonstrates end-to-end model development from raw text to evaluation.

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📂 Project Structure

├── tweets.txt        # Your labeled tweet dataset
├── sentiment_analysis.py  
└── README.md                

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✅ Features

• 📦 Logistic Regression from scratch
• 📉 Manual Gradient Descent for weight updates
• 🧹 Text Preprocessing & Word Frequency Vectorization
• 📊 Loss vs Epochs graph
• 🔁 Parameter Convergence plots
• 📋 Evaluation with Confusion Matrix & Custom Metrics

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📁 Input Format

The dataset file tweets.txt should contain tweets in the following format:

I love this product || Positive  
This is the worst thing ever || Negative  

Each line contains a tweet and its sentiment label (Positive or Negative) separated by ||.

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🧮 Time Complexity

Component	Complexity	Description
Vocabulary Build	O(N × L)	N = # of tweets, L = avg. words per tweet
Vectorization	O(N × V)	V = vocabulary size
Training Loop	O(E × N × V)	E = # of epochs (includes gradient computation)
Evaluation	O(N × V)	Same as vectorization for test set

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🧠 Model Overview

We use a logistic regression model where:

$$sigmoid(z) = 1 / (1 + exp(-z)) z = bias + w1 * pos_freq + w2 * neg_freq$$

Gradient Descent Weight Update:

error = predicted - actual
w1 -= learning_rate * error * pos_freq
w2 -= learning_rate * error * neg_freq
bias -= learning_rate * error

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📈 Output Graphs

• 📉 Loss vs Epochs: Shows how training error decreases over time
• 📍 Parameter Convergence: Plots w1, w2, and bias vs loss with circle markers for better interpretability

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📊 Evaluation Metrics

Evaluation is done using a custom implementation, without any external libraries:

• ✅ Accuracy
• 🔁 Precision
• 🎯 Recall
• 🧮 F1 Score
• 🧮 Confusion Matrix

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🛠️ Requirements

• Python 3.6+
• matplotlib (for plotting)

pip install matplotlib

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🚀 How to Run

python sentiment_analysis.py

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🧪 Example Output

Weights: w1=0.45, w2=-0.27, bias=0.62
Confusion Matrix:
[[7, 2],
 [1, 10]]
Accuracy: 0.85
Precision: 0.83
Recall: 0.91
F1 Score: 0.87

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🙌 Credits

Created by Waqar
Inspired by hands-on ML principles and low-level learning

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