labrijisaad / twitter-sentiment-analysis-with-python

I aim in this project to analyze the sentiment of tweets provided from the Sentiment140 dataset by developing a machine learning sentiment analysis model involving the use of classifiers. The performance of these classifiers is then evaluated using accuracy and F1 scores.

Twitter Sentiment Analysis using Python 🐍 and NLP 📙

🚀 Project Overview

Welcome to the Twitter Sentiment Analysis project! 🌟 Here, we dive into the captivating realm of Natural Language Processing (NLP) to analyze tweet sentiments using mighty machine learning techniques.

📊 Dataset

Access the dataset here: Sentiment140 Dataset. 📂

🛠️ Methodology

We wield the power of classifiers to craft an effective sentiment analysis model, evaluating their prowess with accuracy and F1 scores. 🔍

Getting Started 🏁

Follow these simple steps to set up and start working on the project:

1. Clone the Repository:

   git clone https://github.com/labrijisaad/Twitter-Sentiment-Analysis-with-Python.git

2. Navigate to the Project Directory:

   cd Twitter-Sentiment-Analysis-with-Python

3. Check Python Version: Ensure that you have Python 3.9 installed. You can find the required packages in the requirements.txt file.

4. Create a Virtual Environment (recommended for project isolation):

   python3 -m venv venv

5. Activate the Virtual Environment:

   • For macOS/Linux:

     source venv/bin/activate

   • For Windows:

     venv\Scripts\activate

6. Install Dependencies from requirements.txt:

   pip install -r requirements.txt

7. Download the Dataset: Download the dataset from Sentiment140 Dataset and place the CSV file in a newly created data directory within the project.

8. Launch Jupyter Notebook: Start the Jupyter Notebook server:

   jupyter notebook

🙏 Acknowledgments

This project was inspired by the helpful work of analyticsvidhya. 🎩🙌

📞 Contact

For any queries, suggestions, or virtual high-fives, feel free to reach out at [email protected]. 📬