NLP Sentiment Analysis Tutorial

Welcome to the NLP Sentiment Analysis Tutorial repository! This project is designed to help you understand and implement sentiment analysis using Python and popular NLP libraries. It's beginner-friendly and provides a structured approach to learning sentiment analysis step-by-step.

Project Structure

nlp-sentiment_analysis/
├── main.ipynb        # Main notebook with code and explanations
├── utils.py          # Utility functions for text processing and helper function
├── requirements.txt  # List of dependencies
└── README.md         # Project documentation

Features

• Step-by-Step Guide: Walkthrough of key sentiment analysis concepts.
• Preprocessing Utilities: Helper functions in utils.py.
• Hands-On Notebook: main.ipynb contains a detailed tutorial and examples.

Getting Started

Prerequisites

Make sure you have Python 3.7 or higher installed. You'll also need Jupyter Notebook or Jupyter Lab to run the main.ipynb.

Installation Steps

1. Clone the Repository

   Clone the repository to your local machine using:

   git clone https://github.com/jumarubea/nlp-sentiment_analysis.git
   cd nlp-sentiment_analysis

2. Set Up a Virtual Environment (Optional but Recommended)

   Create a virtual environment to isolate dependencies:

   python -m venv venv
   source venv/bin/activate        # On MacOS/Linux
   venv\Scripts\activate           # On Windows

3. Install Dependencies

   Install the required Python libraries using pip:

   pip install -r requirements.txt

4. Launch Jupyter Notebook

   Start the Jupyter Notebook server and open main.ipynb:

   jupyter notebook

   Navigate to the folder and click on main.ipynb to get started.

Additional Tools

We are going to use twitter_samples datasets and english stopwords by installing them directly to from nltk

nltk.download('twitter_samples')

nltk.download('stopwords')

Workflow

1. Data Loading: Import text data for analysis (examples provided in the notebook).
2. Text Preprocessing: Utilize utilities from utils.py for tasks like tokenization, stopword removal, and lemmatization.
3. Sentiment Analysis: Apply machine learning or rule-based methods to classify sentiments.
4. Visualization: Plot sentiment distributions and other insights using tools like Matplotlib and Seaborn.

Dependencies

This project uses the following Python libraries:

• nltk - For natural language processing.
• numpy - For numerical computations.
• pandas - For data manipulation and analysis.
• pprint - For pretty print of json files

To install these, run:

pip install -r requirements.txt

Contribution

Contributions are welcome! If you’d like to improve this tutorial or fix issues, feel free to fork the repository and submit a pull request.

Acknowledgments

Special thanks to the open-source community for the libraries used in this project.