https://github.com/labrijisaad/twitter-sentiment-analysis-with-python

We 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.
https://github.com/labrijisaad/twitter-sentiment-analysis-with-python

accuracy-score bernoulli-naive-bayes confusion-matrix f1-score lemmatization logistic-regression machine-learning nlp roc-auc-curve sentiment-analysis sentiment140-dataset stemming support-vector-machine tokenization twitter-sentiment-analysis

Last synced: 14 days ago
JSON representation

We 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.

• Host: GitHub
• URL: https://github.com/labrijisaad/twitter-sentiment-analysis-with-python
• Owner: labrijisaad
• Created: 2022-02-27T22:23:43.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-08-13T22:56:45.000Z (over 1 year ago)
• Last Synced: 2023-08-13T23:37:57.039Z (over 1 year ago)
• Topics: accuracy-score, bernoulli-naive-bayes, confusion-matrix, f1-score, lemmatization, logistic-regression, machine-learning, nlp, roc-auc-curve, sentiment-analysis, sentiment140-dataset, stemming, support-vector-machine, tokenization, twitter-sentiment-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 8.87 MB
• Stars: 7
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# 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](https://drive.google.com/file/d/19IeqXU96-kDt6wy1wTNyhWrIw1jbK2Kx/view?usp=sharing). 📂

## 🛠️ **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**:

   ```bash

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

   ```

   

2. **Navigate to the Project Directory**:

   ```bash

   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):

   ```bash

   python3 -m venv venv

   ```

   

5. **Activate the Virtual Environment**:

   - For macOS/Linux:

     ```bash

     source venv/bin/activate

     ```

   

   - For Windows:

     ```bash

     venv\Scripts\activate

     ```

6. **Install Dependencies** from `requirements.txt`:

   ```bash

   pip install -r requirements.txt

   ```

7. **Download the Dataset**:

   Download the dataset from [Sentiment140 Dataset](https://drive.google.com/file/d/19IeqXU96-kDt6wy1wTNyhWrIw1jbK2Kx/view?usp=sharing) and place the CSV file in a newly created `data` directory within the project.

8. **Launch Jupyter Notebook**:

   Start the Jupyter Notebook server:

   ```bash

   jupyter notebook

   ```

## 🙏 **Acknowledgments**

This project was inspired by the helpful work of [analyticsvidhya](https://www.analyticsvidhya.com/). 🎩🙌

## 📞 **Contact**

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