Sentiment Analysis Twitter

Abstract

1. Introduction

This project implements a complete machine learning pipeline starting from raw tweet data and ending with a deployable web application.

This is a multiclass text classification problem on noisy social media data.

An end-to-end Natural Language Processing (NLP) project that classifies tweets into Positive, Negative, or Neutral sentiments using TF-IDF features and Logistic Regression, with a Streamlit-based web application for real-time sentiment inference.

2. Objective

Twitter Sentiment Analysis using Machine Learning with Streamlit UI

This project demonstrates how Python can be applied to a real-world Digital Marketing & SEO problem.

3. Key Features / Modules

• Unigrams and bigrams (ngram_range=(1,2))
• Maximum features limited to 20,000
• Rare and overly common words filtered
• Sublinear term frequency scaling applied

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Mahima07-mai/Sentiment-Analysis-twitter.git
cd Sentiment-Analysis-twitter

pip install -r requirements.txt

streamlit run app.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Export reports to Google Sheets or PDF
• Schedule weekly automated reports
• Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which marketing or SEO problem does this tool solve?
2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
3. Which metrics or KPIs does it report and how are they calculated?
4. How could the output help a business make decisions?
5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by Mahima07-mai and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.