Sentiment Analysis Twitter Model

Abstract

1. Introduction

The build-sentiment-classifier.ipynb Jupyter Notebook builds and exports a serialized Twitter sentiment classifier using Python with scikit-learn. The classifier is based on the approach of Go et al using the Sentiment140 data. The data can be downloaded from the Sentiment140 website.

The classifier has an accuracy of 84% on the test dataset consisting of several hundred annotated tweets. The training set consists of 1.6 million tweets automatically labeled by assuming that any tweet with positive emoticons, like :), were positive, and tweets with negative emoticons, like :(, were negative. This technique is called distant supervision using emoticons as noisy labels.

2. Objective

Build an accurate sentiment model using Python with scikit-learn

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

4. Technology Stack

5. System Requirements

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

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/crawles/sentiment_analysis_twitter_model.git
cd sentiment_analysis_twitter_model

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 crawles 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.