Machine learning project

🐦 Twitter Sentiment Analysis using Logistic Regression

This project performs sentiment analysis on tweets using a Logistic Regression classifier. It classifies tweets into Positive, Negative, or Neutral sentiments based on their text content.

The model is trained using Python in Google Colab, and saved as a .sav file using pickle.

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📁 Project Structure

📊 Dataset

The project uses a labeled Twitter dataset containing tweets and their corresponding sentiment classes. Preprocessing steps include:

• Lowercasing text
• Removing URLs, mentions, and punctuation
• Removing stopwords
• Tokenization and vectorization using TfidfVectorizer

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⚙️ Model

• Algorithm: Logistic Regression (scikit-learn)
• Features: TF-IDF vectors from tweet text
• Target Labels: Positive, Negative, Neutral

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Core Dependencies

• NumPy: For numerical computing and array operations
• Pandas: For data manipulation and analysis
• Scikit-learn: For machine learning and statistical modeling

Installation

1. Clone the repository
2. Install the required dependencies:

pip install -r requirements.txt

Requirements

• Python 3.8+
• See requirements.txt for specific package versions

Getting Started

import numpy as np
import pandas as pd
from sklearn import model_selection