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Hate_Speech_Detection_NLP

hate

This is Hate speech detection model created using Count Vectorizer and XGBoost Classifier with an Accuracy upto 0.9471, train-test split of 70:30, which can be used to predict whether tweets are hate or non-hate.

Dataset:

  • Dataset using Twitter data, isused to research hate-speech detection. The text is classified as: hate-speech, offensive language, and neither. Due to the nature of the study, it’s important to note that this dataset contains text that can be considered racist, sexist, homophobic, or generally offensive.

  • Link for dataset: https://www.kaggle.com/mrmorj/hate-speech-and-offensive-language-dataset

Tools used for project development:

  • Python

  • NLP

  • Porter Stemmer

  • Count Vectorizer

  • XGBoost Classifier

  • Random Forest Classifier

  • Decision Tree

  • Support Vector Machine

  • Logistic Regression

  • K Nearest Neighbours

  • Gaussian Naive Bayes Classifier

If you like this repo, please don't forget to give a ⭐.