Sex, drugs, and violence
Abstract
Automatically detecting inappropriate content can be a difficult NLP task, requiring understanding context and innuendo, not just identifying specific keywords. Due to the large quantity of online user-generated content, automatic detection is becoming increasingly necessary. We take a largely unsupervised approach using a large corpus of narratives from a community-based self-publishing website and a small segment of crowd-sourced annotations. We explore topic modelling using latent Dirichlet allocation (and a variation), and use these to regress appropriateness ratings, effectively automating rating for suitability. The results suggest that certain topics inferred may be useful in detecting latent inappropriateness -- yielding recall up to 96% and low regression errors.
Keywords
Cite
@article{arxiv.1608.03448,
title = {Sex, drugs, and violence},
author = {Stefania Raimondo and Frank Rudzicz},
journal= {arXiv preprint arXiv:1608.03448},
year = {2016}
}