├── Dataset.json ├── Notebook.ipynb ├── ProjectReport.pdf └── README.md /ProjectReport.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kirtiksingh/Cyberbullying-Detection-using-Machine-Learning/24db85cf4ace47376bc433edef3cb05a52f884d1/ProjectReport.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![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) 2 |

3 |

4 | 5 | # Cyberbullying-Detection-using-Machine-Learning 6 | Cyberbullying Detection: Identifying Hate Speech using Machine Learning 7 | 8 | ## Description 9 | 10 | Bullying has been prevalent since the beginning of time, It’s just the ways of bullying which 11 | have changed over the years, from physical bullying to 12 | 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. 13 | 14 | This project presents a systematic review of some 15 | published research on cyberbullying detection approaches 16 | and examine methods to detect hate speech in social media, 17 | while distinguishing this from general profanity, and does 18 | a comparative study of various Supervised algorithms, 19 | including standard, as well as ensemble methods. 20 | 21 | ## Dataset 22 | - 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) 23 | - Data Cleaning, Preprocessing (Word Tokenization, Stemming, TF-IDF), and Resampling was done before application of any of the Machine Learning algorithms used. 24 | 25 | ## Methods Used 26 | - Gaussian Naive Bayes 27 | - Logistic Regression 28 | - Decision Tree Classifier 29 | - Adaboost Classifier 30 | - Random Forest Classifier 31 | 32 | 33 | ## Result 34 | 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 35 | future work are also discussed. 36 | 37 | ## Documentation 38 | [Project Report](https://github.com/kirtiksingh/Cyberbullying-Detection-using-Machine-Learning/blob/main/Project%20Report.pdf) 39 | 40 | 41 | ### Authors 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 |

_{Kirtik Singh}

_{Prakhar Bhasin}

_{Dev Kathuria}

_{Ishank Nijhawan}

50 | 51 | --------------------------------------------------------------------------------