A method, comprising: receiving a first document propagating in a network environment, the first document comprising data; separating the data into one or more fields; searching the first document to determine whether the first document includes any words that are included in a blacklist of rejected words; if any of the rejected words are found in the first document, dropping the first document; if the first document is not dropped: filtering the first document to identify a document type of the first document and determine whether the first document does not offer a likelihood of finding substantive vocabulary within the first document, and, if so, ignoring the first document; if the first document is not ignored: extracting noun phrases, which comprise at least a noun or a pronoun, from any of the one or more fields that are text fields; searching the text fields based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged; tagging any noun phrases that correspond to the designated words in the whitelist; generating a resultant composite of the noun phrases that are tagged; creating a concept field including the extracted noun phrases; creating a second document including only the concept field; removing words from the second document which are predetermined stop words; reading the concept field from the second document and calculating a weight of each topic in the concept field; outputting the topics and the calculated weights; aggregating the topics into a collapsed vocabulary table; and reading the collapsed vocabulary table to present a top list of terms to an administrator as suggestions for adding to the whitelist. receiving a first document propagating in a network environment, the first document comprising data; separating the data into one or more fields; searching the first document to determine whether the first document includes any words that are included in a blacklist of rejected words; if any of the rejected words are found in the first document, dropping the first document; if the first document is not dropped: filtering the first document to identify a document type of the first document and determine whether the first document does not offer a likelihood of finding substantive vocabulary within the first document, and, if so, ignoring the first document; if the first document is not ignored: extracting noun phrases, which comprise at least a noun or a pronoun, from any of the one or more fields that are text fields; searching the text fields based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged; tagging any noun phrases that correspond to the designated words in the whitelist; generating a resultant composite of the noun phrases that are tagged; creating a concept field including the extracted noun phrases; creating a second document including only the concept field; removing words from the second document which are predetermined stop words; reading the concept field from the second document and calculating a weight of each topic in the concept field; outputting the topics and the calculated weights; aggregating the topics into a collapsed vocabulary table; and reading the collapsed vocabulary table to present a top list of terms to an administrator as suggestions for adding to the whitelist. filtering the first document to identify a document type of the first document and determine whether the first document does not offer a likelihood of finding substantive vocabulary within the first document, and, if so, ignoring the first document; if the first document is not ignored: extracting noun phrases, which comprise at least a noun or a pronoun, from any of the one or more fields that are text fields; searching the text fields based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged; tagging any noun phrases that correspond to the designated words in the whitelist; generating a resultant composite of the noun phrases that are tagged; creating a concept field including the extracted noun phrases; creating a second document including only the concept field; removing words from the second document which are predetermined stop words; reading the concept field from the second document and calculating a weight of each extracting noun phrases, which comprise at least a noun or a pronoun, from any of the one or more fields that are text fields; searching the text fields based on a whitelist, wherein the whitelist includes a plurality of designated words to be tagged; tagging any noun phrases that correspond to the designated words in the whitelist; generating a resultant composite of the noun phrases that are tagged; creating a concept field including the extracted noun phrases; creating a second document including only the concept field; removing words from the second document which are predetermined stop words; reading the concept field from the second document and calculating a weight of each topic in the concept field; outputting the topics and the calculated weights; aggregating the topics into a collapsed vocabulary table; and reading the collapsed vocabulary table to present a top list of terms to an administrator as suggestions for adding to the whitelist. outputting the topics and the calculated weights; aggregating the topics into a collapsed vocabulary table; and reading the collapsed vocabulary table to present a top list of terms to an administrator as suggestions for adding to the whitelist.