1. A method for determining a diaculture of text, comprising: tokenizing words of the text with one or more processors according to a rule set to generate tokenized text, the rule set defining: a first set of grammatical types of words, which are words that are replaced, in the tokenizing, with tokens that respectively indicate a grammatical type of a respective word, and a second set of grammatical types of words, which are words that are passed, in the tokenizing, as tokens without changing; constructing grams from the tokenized text, each gram including one or more of consecutive tokens from the tokenized text; comparing the grams to a training data set that corresponds to a known diaculture to obtain a comparison result that indicates how well the text matches the training data set for the known diaculture, wherein the training data set includes a plurality of comments written by authors of the known diaculture; tokenizing words of the comments with one or more processors according to the rule set to generate tokenized comments; constructing grams from the tokenized comments, each gram including one or more of consecutive tokens from the tokenized comments; assigning scores to each of the grams of the tokenizing comments according to one or more scoring methods that each define a relationship between a score of a gram, and a number of times the gram appears in the training data set and a baseline data set; and assigning scores to the grams of the tokenized text based on the scores assigned to the grams of the tokenized comments.
1. A method for determining a diaculture of text, comprising: tokenizing words of the text with one or more processors according to a rule set to generate tokenized text, the rule set defining: a first set of grammatical types of words, which are words that are replaced, in the tokenizing, with tokens that respectively indicate a grammatical type of a respective word, and a second set of grammatical types of words, which are words that are passed, in the tokenizing, as tokens without changing; constructing grams from the tokenized text, each gram including one or more of consecutive tokens from the tokenized text; comparing the grams to a training data set that corresponds to a known diaculture to obtain a comparison result that indicates how well the text matches the training data set for the known diaculture, wherein the training data set includes a plurality of comments written by authors of the known diaculture; tokenizing words of the comments with one or more processors according to the rule set to generate tokenized comments; constructing grams from the tokenized comments, each gram including one or more of consecutive tokens from the tokenized comments; assigning scores to each of the grams of the tokenizing comments according to one or more scoring methods that each define a relationship between a score of a gram, and a number of times the gram appears in the training data set and a baseline data set; and assigning scores to the grams of the tokenized text based on the scores assigned to the grams of the tokenized comments. 14. The method according to claim 1 , wherein the comparing includes: windowing a fixed number of the consecutive tokens in the tokenized text to form a first window, and repeatedly advancing the first window by one token to form a plurality of windows of tokens from the tokenized text; assigning scores to the windows based on the scores assigned to the grams; and obtaining the comparison result based on the scores assigned to the windows.