Skip to content

Latest commit

 

History

History
58 lines (43 loc) · 2.08 KB

README.md

File metadata and controls

58 lines (43 loc) · 2.08 KB

Twitter Sentiment Analysis using Logistic Regression

This project is a Twitter sentiment analysis tool that uses logistic regression to classify tweets as either positive or negative. The project is structured into three main components: function definitions, model training, and user interaction for testing the model or viewing the accuracy plot.

Project Structure

  • functions.py: Contains all the necessary functions for tweet processing, logistic regression, and prediction.
  • train.py: Handles data loading, model training, and saves the trained model.
  • test_or_plot.py: Offers an interactive way to test new tweets or show a plot of model accuracy.

Getting Started

Prerequisites

Before running this project, you need to install Python and the necessary libraries. You can install the required dependencies using:

pip install -r requirements.txt

Installation

    1. Clone the repo:
git clone https://github.com/mouraffa/Sentimental_Analysis_LogRegClassification.git

*2. Install the required packages

pip install -r requirements.txt

Usage

Run train.py to train the model and save the parameters:

python train.py

After training the model, you can either test it with your own tweets or view the accuracy plot by running:

python test_or_plot.py

How it Works

  • train.py downloads tweet data, processes it, and uses it to train a logistic regression model.
  • test_or_plot.py allows you to input a custom tweet to get a sentiment prediction or to view a plot of the model's training accuracy.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

    1. Fork the Project
    1. Create your Feature Branch (git checkout -b feature/AmazingFeature)
    1. Commit your Changes (git commit -m 'Add some AmazingFeature')
    1. Push to the Branch (git push origin feature/AmazingFeature)
    1. Open a Pull Request

Contact

LinkedIn Email