Anomaly detection in data object text using natural language processing (nlp)

A method, comprising

• receiving a first data object that corresponds to a first computing service and that includes first text content;
• accessing a trained anomaly classifier, wherein the trained anomaly classifier was trained based on an artificial neural network by using a natural language processing algorithm on respective text content of at least a portion of each of a plurality of data objects corresponding to the first computing service, and wherein each of the plurality of data objects is labeled as belonging to one of a plurality of categories;
• identifying, using the trained anomaly classifier, one or more text character sequences in the first text content of the first data object as anomalous;
• and
• outputting identifying information indicating the one or more anomalous text character sequences in the first text content of the first data object.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• receiving a first data object that corresponds to a first computing service and that includes first text content;
• accessing a trained anomaly classifier, wherein the trained anomaly classifier was trained based on an artificial neural network by using a natural language processing algorithm on respective text content of at least a portion of each of a plurality of data objects corresponding to the first computing service, and wherein each of the plurality of data objects is labeled an error condition or a no error condition;
• identifying, using the trained anomaly classifier, one or more text character sequences in the first text content of the first data object as anomalous;
• and
• outputting identifying information indicating the one or more anomalous text character sequences in the first text content of the first data object.