Sentiment Analysis using Machine Learning

📌 Project Overview

This project is a simple Sentiment Analysis system that classifies text into:

• Positive
• Negative
• Neutral

It uses Natural Language Processing (NLP) and Machine Learning to analyze user reviews.

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🚀 Problem Statement

Companies receive thousands of user reviews daily.
Manually analyzing them is difficult.

This system automates sentiment detection from text data.

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🧠 How It Works

Pipeline:

Text → Cleaning → Tokenization → Vectorization → Model → Prediction

• Text Cleaning: Removes noise (punctuation, symbols)
• Tokenization: Breaks text into words
• Vectorization: Converts text into numbers (CountVectorizer)
• Model: Multinomial Naive Bayes

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🛠️ Tech Stack

• Python
• Pandas
• Scikit-learn
• Joblib

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📂 Project Structure

dl-sentiment-analysis/
│
├── data/
├── models/
│ ├── sentiment_model.pkl
│ └── vectorizer.pkl
│
├── notebook/
│ └── sentiment_analysis.ipynb
│
├── src/
│ └── predict.py
│
├── README.md

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▶️ How to Run

1. Clone the repo
2. Navigate to project folder
3. Run:

python src/predict.py

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Example

Input : This movie is amazing

Output : Sentiment : Positive

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👨‍💻 Author

Abhihail Jacob