1. A method comprising: receiving, from a client device of a user, textual feedback associated with a metadata and a provider, the textual feedback including a plurality of words, the textual feedback describing a transportation service provided by the provider for the user; determining a feature vector for the textual feedback by characterizing the plurality of words and the metadata; determining a first safety sub-score by applying the feature vector to a first classifier associated with a first category of safety risk, the first classifier being a machine learning model trained using a first training feature vector characterizing a first set of textual feedback associated with the first category of safety risk; determining a second safety sub-score by applying the feature vector to a second classifier associated with a second category of safety risk different than the first category of safety risk, the second classifier being a different machine learning model trained using a second training feature vector characterizing a second set of textual feedback associated with the second category of safety risk; and determining a safety score for the provider using a third classifier based, at least in part, on the first safety sub-score and the second safety sub-score.
1. A method comprising: receiving, from a client device of a user, textual feedback associated with a metadata and a provider, the textual feedback including a plurality of words, the textual feedback describing a transportation service provided by the provider for the user; determining a feature vector for the textual feedback by characterizing the plurality of words and the metadata; determining a first safety sub-score by applying the feature vector to a first classifier associated with a first category of safety risk, the first classifier being a machine learning model trained using a first training feature vector characterizing a first set of textual feedback associated with the first category of safety risk; determining a second safety sub-score by applying the feature vector to a second classifier associated with a second category of safety risk different than the first category of safety risk, the second classifier being a different machine learning model trained using a second training feature vector characterizing a second set of textual feedback associated with the second category of safety risk; and determining a safety score for the provider using a third classifier based, at least in part, on the first safety sub-score and the second safety sub-score. 6. The method of claim 1 , wherein determining the safety score for the provider using the third classifier is further based on a third safety sub-score determined by a fourth classifier, the fourth classifier associated with the metadata.