Simple neural network to detect offensive language usage

The dataset for this project is obtained from twitter and contains instances of hate speech, offensive language and neither. Seen as I wanted to try with binary classification I will only use the offensive speech and the non offensive to create two classes for the target. The dataset contains labeled text which I will first process and then train a simple neural network to capture the difference between the two kinds of speechs.

• Importing libraries
• The dataset
• Converting characters to lower case
• Removing punctuation
• Removing digits
• Tokenizing sentences
• Removing stopwords
• Removing frequent words
• Stemming
• Bag of words
• Train/Test split
• Creating train/test loaders
• Building the classifier
• Training and inference
• Trying out the model
• Conclusion

• Pandas
• Numpy
• Torch
• NLTK
• SKlearn

https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offensive-language-dataset?fbclid=IwAR2kOFFLqhMQrV_K1ThWbpWG21WGPqyn4HOsiLzR3vsXMkQw1rgH9MxGGuc