/Toxic-Comment-Detector

This project applies classification models with the aim of automating the detection of toxic comments on social media. After choosing the model with the best performance, HuggingFace + Streamlit are applied to make the web app.

Primary LanguageJupyter NotebookMIT LicenseMIT

Toxic-Comment-Detector

This project applies classification models with the aim of automating the detection of toxic comments on social media.

Modeling:

Three main Machine Learning classification models are used:

  • Multinomial Naive Bayes
  • Logistic Regression
  • Support Vector Machine (SVM)

According to the results, Naive Bayes classifier with TfidfVectorizer came with the best f1 score and recall: 0,85 and 0.88, respectively. Because of this reason, it was used as the base model of the web application.

Full results:

Models precision recall f1 score
Multinomial NB 0.82 0.88 0.85
Logistic Regression 0.82 0.80 0.81
SVM 0.82 0.83 0.83

Web application:

To make the web app, I used Streamlit to turn the python script into an app. For the final deployment of an interactive model, I used HuggingFace's Spaces.

You can check it out and try it out here.

Screenshot of the web page:

Screenshot of the web page

Data:

Data for this project was obtained from the GitHub repository of SurgeAI, a powerful NLP data labeling platform.

Data Description:

  • Dataset consists of 1000 English examples gathered from different popular social media platform.
  • Each 500 examples in it are labeled as "toxic" and "non-toxic", respectively.

  • Columns:

    • text: the text of the social media comment
    • is_toxic: whether the text is toxic or not

References: