hashformers
hate-speech-and-offensive-language
| hashformers | hate-speech-and-offensive-language | |
|---|---|---|
| 2 | 2 | |
| 63 | 752 | |
| - | - | |
| 6.2 | 1.9 | |
| 11 months ago | 11 months ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
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hashformers
hate-speech-and-offensive-language
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How to make a class column for a classifier from sentiment analysis results?
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):
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Where do we go from here and who is going to step up to help us?
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.
What are some alternatives?
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
toxicity - The world's largest social media toxicity dataset.
NewsMTSC - Target-dependent sentiment classification in news articles reporting on political events. Includes a high-quality data set of over 11k sentences and a state-of-the-art classification model.
cia - 🐱💻 CIA Factbook data analysis and dataset reconstruction, modification, and tuning go here.
FinBERT - A Pretrained BERT Model for Financial Communications. https://arxiv.org/abs/2006.08097
Tegridy-MIDI-Dataset - Tegridy MIDI Dataset for precise and effective Music AI models creation.
should-i-follow - 🦄 An NLP application just for the lols: built with Haystack to get an overview of what a user is posting about on Twitter
ThoughtSource - A central, open resource for data and tools related to chain-of-thought reasoning in large language models. Developed @ Samwald research group: https://samwald.info/
cellpose - a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
airline-sentiment-streaming - Streaming with Airline Sentiment. Utilizing Cloudera Machine Learning, Apache NiFi, Apache Hue, Apache Impala, Apache Kudu
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
100daysofpractice-dataset - Data from Instagram posts with the hashtag #100daysofpractice.