https://github.com/romsto/Inappropriate-Language-Classifier

Online video games need a better system to detect inappropriate language in chat, and using machine learning models could help create a safer and more enjoyable environment for all players.
https://github.com/romsto/Inappropriate-Language-Classifier

deep-learning hate-speech-detection huggingface machine-learning natural-language-processing natural-language-understanding transformers

Last synced: about 2 months ago
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Online video games need a better system to detect inappropriate language in chat, and using machine learning models could help create a safer and more enjoyable environment for all players.

• Host: GitHub
• URL: https://github.com/romsto/Inappropriate-Language-Classifier
• Owner: romsto
• Created: 2023-03-17T14:40:52.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-04-05T19:45:57.000Z (over 1 year ago)
• Last Synced: 2024-06-02T01:37:01.366Z (4 months ago)
• Topics: deep-learning, hate-speech-detection, huggingface, machine-learning, natural-language-processing, natural-language-understanding, transformers
• Language: Jupyter Notebook
• Homepage:
• Size: 14.2 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classification of inappropriate language

## Introduction

The goal of our study is to compare different ML and DL models on classifying text as *appropriate* or *inappropriate language*.

The ultimate goal is to classify in-game chats, but our study will mainly focus on social media texts.

## What is Inappropriate Language ?

We define, for the following work, an *Inappropriate Language* as a word or a sentence that contains any of the following:

- **Profanity** - This includes any language that is considered vulgar, offensive, or obscene. This can include swear words, sexual language, and derogatory terms.

- **Hate speech** - Hate speech is language that is intended to demean, discriminate against, or incite violence or hatred towards a particular group of people based on their race, ethnicity, gender, religion, sexual orientation, or other characteristic.

- **Insults** - This includes any language that is intended to insult or belittle someone else. This can include name-calling, personal attacks, or derogatory comments about someone's appearance, abilities, or personality.

- **Threats** Threats are language that is intended to intimidate or harm another person. This can include physical threats, verbal abuse, or intimidation.

## Models tested

- Decision Tree

    - Source: sklearn

    - Parameters: None

- Random Forest

    - Source: sklearn

    - Parameters: None

- SVC (SVM)

    - Source: sklearn

    - Parameters: None

- LSTM

    - Source: Tensorflow

    - LSTM -> Dense layer (In -> Out (2))

- Transformer (mobile BERT / distil BERT)

    - Source: Huggingface

    - Zero Shot Classification

## Our Dataset

Our dataset is a merge of multiple social media Hate Speech datasets.

### Columns:

- `Text`: text to analyze

- `Class`:

    - `0` - Appropriate

    - `1` - Inappropriate

### Data Sources:

- Hate Speech and Offensive Language (https://github.com/t-davidson/hate-speech-and-offensive-language/raw/master/data/labeled_data.csv) [tweets]

- Measuring Hate Speech (https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech) [social media Reddit Twitter and other]

- https://www.kaggle.com/competitions/detecting-insults-in-social-commentary/

- Jibes and Delights https://github.com/ravsodhi/jibes-and-delights (Insults and Compliments targeting INDIVIDUALS) https://aclanthology.org/2021.woah-1.14.pdf

- https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/ 

### Details:

- English only

- From social media comments