A Comparative Study of Topic Identification on Newspaper and E-mail

This paper presents several statistical methods for topic identification on two kinds of textual data: newspaper articles and e-mails. Five methods are tested on these two corpora: topic unigrams, cache model, TFIDF classifier, topic perplexity, and weighted model. Our work aims to study these methods by confronting them to very different data. This study is very fruitful for our research. Statistical topic identification methods depend not only on a corpus, but also on its type. One of the methods achieves a topic identification of 80% on a general newspaper corpus but does not exceed 30% on e-mail corpus. Another method gives the best result on e-mails, but has not the same behavior on a newspaper corpus. We also show in this paper that almost all our methods achieve good results in retrieving the first two manually annotated labels.