Sarcasm Detection

Overview

Welcome to the Sarcasm Detection repository! This project is dedicated to the development of a sarcasm detection system using natural language processing and machine learning techniques. Sarcasm detection is an essential task in the field of natural language understanding, with applications ranging from sentiment analysis to social media monitoring.

Project Goals

The primary goals of this project include:

Developing a robust and accurate sarcasm detection model.
Exploring various machine learning and deep learning algorithms for sarcasm detection.
Creating an easy-to-use Python library or API for sarcasm detection.
Evaluating the model's performance on different datasets and real-world applications.

Features

Preprocessing and text cleaning utilities.
Implementation of state-of-the-art sarcasm detection models.
Evaluation metrics for model performance.
Example code and notebooks for using the sarcasm detection model.
A user-friendly interface for quick integration.

Getting Started

To get started with sarcasm detection, follow these steps:

Clone this repository to your local machine.

git clone https://github.com/mbuthi/sarcasm_detection_model
cd sarcasm_detection_model

Create a virtual environment using virtualenv (assuming you have virtualenv installed).

pip install virtualenv  # Install virtualenv if not already installed
virtualenv venv         # Create a virtual environment named 'venv'
source venv/bin/activate  # Activate the virtual environment (on Windows, use 'venv\Scripts\activate')

Install the required dependencies by running pip install -r requirements.txt.

pip install -r requirements.txt

Start a Jupyter Notebook server to explore the example notebooks.

jupyter notebook

This will open a web browser window with access to the Jupyter Notebook interface.

Explore the example notebooks to see how the sarcasm detection model works and use the provided utilities to preprocess and analyze text data.

Contact

If you have any questions, suggestions, or feedback, please feel free to reach out. You can find me on Twitter.

I hope this repository proves to be a valuable resource for sarcasm detection and helps improve understanding and interpretation of sarcastic text in various applications.