hate-speech-and-offensive-language VS cia

I've used NRCLex to perform sentiment analysis on some Twitter data. I have hate speech classifier code (https://github.com/t-davidson/hate-speech-and-offensive-language/blob/master/classifier/final_classifier.ipynb) I want to pass the dataset through, but before I can I need to have a "class" column for the model. For those not familiar, NRCLex returns scores for 10 emotions: anticipation, joy, anger, fear, surprise, disgust, positive, negative, sadness and trust. The table looks like this (letters denoting emotions):

Some of this exists, and both Quora and Facebook (among others) use it extensively. Both hate speech and porn are good targets for machine learning. It needs supervision, but it can take a lot of load off human moderators.

Open source implementations exist, e.g.:

https://github.com/t-davidson/hate-speech-and-offensive-lang...

I suspect more message board will want to start applying these sooner rather than later. Most have already figured out that they need anti-spam tools, rather than it coming as a surprise when they roll things out and it fills up with bots. The technology is similar.

You mention being able to share that information across boards, and I don't know of any widespread implementation of that. You can, at least, let somebody else handle your authentication, which slightly slows their ability to create new accounts when you blacklist one. I'd like to see those sites distinguish "aged" accounts, so that it at least takes some effort or cost to use a new account.

While you can download and use this dataset for free through https://github.com/woosal1337/cia, you can also prefer using Kaggle Page, whereas both of the pages are going to stay updated to the latest versions accordingly.

For each separate file, folder please click here, if you want to visit the file where all of the columns were combined together (over 66 columns), then please click here.