Twitter Sentiment Analysis

DS Club Participants used a machine learning algorithm to train a model that classifies tweets into four categories:

1. Positive
2. Negative
3. Neutral
4. Irrelevant

This project involves training a sentiment analysis model on Twitter data, followed by deploying it as a user-friendly web application for real-time sentiment prediction.

Project Overview

This repository contains the scripts and datasets used for training and deploying the sentiment analysis model. The model was trained on a labeled dataset of tweets, using features engineered to capture the sentiment of each tweet. After training, the model was integrated into a web application using Streamlit, making it easy for users to input text and receive instant sentiment predictions.

Repository Contents

• headers_only.csv: A CSV file with headers only, presumably used as a template or for dataset structure reference.
• train_set.ipynb: A Jupyter notebook used for training the sentiment analysis model.
• twitter_sentiment.ipynb: A Jupyter notebook for experimenting with or testing the sentiment analysis model.
• twitter_streamlit.py: A Streamlit application script to deploy the model as a web app, allowing users to input tweet text and see the predicted sentiment.
• twitter_training.csv: The main dataset used for training the model, containing tweets and their associated sentiment labels.
• twitter_validation.csv: A validation dataset to evaluate the performance of the model.

Getting Started

Prerequisites

To run this project, you will need Python installed with the following packages:

• Pandas
• Scikit-learn
• Streamlit
• Any other dependencies specified in your training or Streamlit scripts

Installation

1. Clone the repository:

   git clone https://github.com/your-username/sentiment_analysis_twitter.git

2. Install dependencies::

   pip install -r requirements.txt

3. Running the Training Script: To train the model, open and execute the train_set.ipynb notebook. This notebook will walk you through the data preprocessing, model training, and evaluation steps.
4. Launching the Web App:

   streamlit run twitter_streamlit.py

App will launch automatically in your browser.

Usage:

The web app provides an easy-to-use interface for analyzing the sentiment of tweets. Simply enter a tweet, and the app will classify it as Positive, Negative, Neutral, or Irrelevant.

Contributors

DS Club 1.0 Participants from Makerspace Petropavl This project was developed by DS Club Participants as part of a club initiative to explore machine learning applications in sentiment analysis.

License

This project is licensed under the MIT License.