Unsupervised multi-dimensional computer-generated log data anomaly detection

A system for identifying anomalies in log data which comprises:

• a computer system comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
• receive a plurality of discrete log data entries, said plurality having at least one unique entry;
• generate a log data sentiment analyzer having a lexicon customized to identify a tone of content for each of said log data entries;
• assign a unique message ID to said at least one unique message;
• collect data attributes of said at least one unique entry, said attributes identifying, for each unique entry, an entry sentiment and at least one additional unique entry attribute;
• and
• conduct statistical analysis of said collected data attributes using at least one deep learning analysis model to identify historical anomalies in said log data attributes, wherein said identified anomalies indicate trouble associated with said log data.

A computer program product to identify anomalies in log data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

• receive, using the computer, a plurality of discrete log data entries, said plurality having at least one unique entry;
• generate, using the compouter, a log data sentiment analyzer having a lexicon customized to identify a tone of content for each of said log data entries;
• assign, using the computer, a unique message ID to said at least one unique message;
• collect, using the computer, data attributes of said at least one unique entry, said attributes identifying, for each unique entry, an entry sentiment and at least one additional unique entry attribute;
• and
• conduct, using the computer, statistical analysis of said collected data attributes using at least one deep learning analysis model to identify historical anomalies in said log data attributes, wherein said identified anomalies indicate trouble associated with said log data.