Intelligent classification of text-based content

A computer system comprising one or more processing units and memory, wherein the computer system is configured to perform operations comprising, with a software application:

• receiving text-based content comprising a plurality of characters;
• converting respective characters of the text-based content into corresponding character category identifiers based on a plurality of predefined character categories, thereby generating a series of character category identifiers;
• generating a plurality of character category sequences based on the series of character category identifiers, wherein each character category sequence comprises multiple character category identifiers identifying respective ones of the plurality of predefined character categories;
• calculating a frequency distribution of the plurality of character category sequences, wherein the frequency distribution represents relative frequencies of occurrence of unique character category sequences among a total number of the plurality of character category sequences;
• and
• classifying, using a machine learning model, the text-based content into one of multiple classes based on the calculated frequency distribution of the plurality of character category sequences,
• wherein a total number of the plurality of predefined character categories is smaller than a total number of possible unique characters such that dimension of input to the machine learning model is reduced as compared with input based on a frequency distribution of character sequences for the text-based content.

A computer system comprising one or more processing units and memory, wherein the computer system is configured to perform operations comprising, with a software application:

• receiving text-based content comprising a plurality of characters;
• scanning the plurality of characters of the text-based content on a character-by-character basis and, for each of a plurality of character sequences of the text-based content, determining a corresponding character category sequence among a plurality of character category sequences, wherein each character category sequence comprises multiple character category identifiers that identify respective ones of a plurality of predefined character categories;
• calculating a frequency distribution of the plurality of character category sequences, wherein the frequency distribution represents relative frequencies of occurrence of unique character category sequences among a total number of the plurality of character category sequences;
• and
• classifying, using a machine learning model, the text-based content into one of multiple classes based on the calculated frequency distribution of the plurality of character category sequences,
• wherein a total number of the plurality of predefined character categories is smaller than a total number of possible unique characters such that dimension of input to the machine learning model is reduced as compared with input based on a frequency distribution of character sequences for the text-based content.