Using term-frequency-inverse-document frequency of email to detect change in social groups

摘要

Interest in the classification of large text data sets continues to grow. The Enron email corpus remains a rich source of inquiry given its size and the unique characteristic of email: timestamps. Using this temporal data, we study a method for detecting temporal changes in the classifications. Using term-frequency-inverse-document-frequency (TFIDF) upon which we apply a change detection algorithm (CUSUM), our results suggest a methodology for predicting changes in email conversations in a social network.