The challenges of a keyword classifier is, in summary, that the classification does not account for context. We can overcome this challenge to a certain extent by applying a classification that uses a large language model (or LLM). LLMs are trained on large amounts of data to learn how language works. They can then use this knowledge to perform a variety of natural language processing (NLP) tasks, including classifying text.
Put simply for our use case: LLMs are much better at classifying complicated things such as broad topics (”politics” or “gender”) or tone (“intimidation” or “negative sentiment” or “toxicity”).
When you click “create” in the Classify tab on the Phoenix platform, you will see options to create an author classifier and a keyword text classifier (these two options are described in the previous sections). You will also see options to apply complex models to classify text (in posts or comments), with the name of the model and a short description. The models listed below are currently available on Phoenix. Click on the dropdown button for information on how they work
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Can’t see a model that works for you? We aim to add more classifier models as we grow. Do you have access to a model that you think would help Phoenix? Email [email protected] to let us know and we’ll try to integrate it!
It is possible for peacebuilders to train their own models, here is how we think about this process. We work with a community of developers interested in building models relevant to conflict analysis; you can join our Discord here to find collaborators.
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