Cyberbullying-Detection-using-Machine-Learning

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

Cyberbullying-Detection-using-Machine-Learning

(★ 26)

Cyberbullying Detection: Identifying Hate Speech using Machine Learning

Cyberbullying-Detection-using-Machine-Learning 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

![Cyberbullying-Detection-using-Machine-Learning](https://socialify.git.ci/kirtiksingh/Cyberbullying-Detection-using-Machine-Learning/image?font=Inter&language=1&owner=1&pattern=Circuit%20Board&theme=Dark)
<img alt="Python" src="https://img.shields.io/badge/python-%2314354C.svg?style=for-the-badge&logo=python&logoColor=white"/>
<img alt="Jupyter" src="https://img.shields.io/badge/Jupyter-%23F37626.svg?style=for-the-badge&logo=Jupyter&logoColor=white" />

# Cyberbullying-Detection-using-Machine-Learning
Cyberbullying Detection: Identifying Hate Speech using Machine Learning

## Description

Bullying has been prevalent since the beginning of time, It’s just the ways of bullying which
have changed over the years, from physical bullying to
cyberbullying. Due to the massive rise of user-generated web content, particularly on social media networks, the amount of hate speech is steadily increasing. Hate speech online has been linked to a global increase in violence toward minorities, including mass shootings, lynchings, and ethnic cleansing.

This project presents a systematic review of some
published research on cyberbullying detection approaches
and examine methods to detect hate speech in social media,
while distinguishing this from general profanity, and does
a comparative study of various Supervised algorithms,
including standard, as well as ensemble methods.

## Dataset 
- Tweets Dataset for Detection of Cyber-Trolls obtained from [DataTurks](https://www.kaggle.com/dataturks/dataset-for-detection-of-cybertrolls?select=Dataset+for+Detection+of+Cyber-Trolls.json)
- Data Cleaning, Preprocessing (Word Tokenization, Stemming, TF-IDF), and Resampling was done before application of any of the Machine Learning algorithms used.

## Methods Used 
- Gaussian Naive Bayes 
- Logistic Regression
- Decision Tree Classifier
- Adaboost Classifier
- Random Forest Classifier

## Result
The evaluation of the result shows that Ensemble supervised methods have the potential to perform better than traditional supervised methods. A number of directions for
future work are also discussed.
  
## Documentation
[Project Report](https://github.com/kirtiksingh/Cyberbullying-Detection-using-Machine-Learning/blob/main/Project%20Report.pdf)

### Authors 
<table>
 <tr>
 <td align="center"><a href="https://github.com/kirtiksingh"><img src="https://avatars.githubusercontent.com/u/42700919?v=4" width="100px;" alt="" style="border-radius:50%"/> Kirtik Singh</a> </td>
 <td align="center"><a href="https://github.com/prakharbhasin"><img src="https://avatars.githubusercontent.com/u/61727284?v=4" width="100px;" alt="" style="border-radius:50%"/> Prakhar Bhasin</a> </td>
 <td align="center"><a href="https://github.com/Dev-eloperr"><img src="https://avatars.githubusercontent.com/u/27141130?v=4" width="100px;" alt="" style="border-radius:50%"/> Dev Kathuria</a> </td>
 <td align="center"><a href="https://github.com/ishanknijhawan"><img src="https://avatars.githubusercontent.com/u/45118110?v=4" width="100px;" alt="" style="border-radius:50%"/> Ishank Nijhawan</a> </td>
 </tr>
</table>

Cyberbullying-Detection-using-Machine-Learning

Cyberbullying-Detection-using-Machine-Learning 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Cyberbullying-Detection-using-Machine-Learning

Cyberbullying-Detection-using-Machine-Learning 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README