Federal government websites often end in. The site is secure. Preview improvements coming to the PMC website in October Learn More or Try it out now. There is increasing interest in automatically identifying advertisements related to sex trafficking in online review sites.
The main challenge is to identify the changing patterns in text reviews that are used to indicate illegal businesses. This work describes a novel means of identifying illegal business advertisements using natural language processing and machine learning.
The method relies on building a training set of reviews of known illegal businesses. This training data is created by integrating a small high precision set of known illegal businesses Rubmaps with a large collection of online reviews from a general purpose review site Yelp. Standard natural language pre-processing techniques are then applied to the text reviews and converted into a bag-of-words model with Term frequency-inverse document weighting.
The resulting Document-Term matrix is used to train a classifier and then to identify suspicious activity from the remaining reviews. This approach therefore leverages a high-precision, low-recall dataset to identify relevant instances from the large low-precision, high-recall dataset. The approach was evaluated on a collection of , reviews from the Yelp online forum with a variety of machine learning algorithms and different number of text features.
The method achieved a f1-score of 0. The number of text features could also be reduced from 1, to for a compact classifier with only a small drop in accuracy. According to the Polaris Project, greater than 9, illicit massage business are operational in the United States alone [ 1 ].
