Efficiency of TR-Classifier versus TFIDF

In this paper, we present a method of topic identification based on computing triggers pairs: TR-classifier (Triggers-based classifier). Indeed, it is used for the purpose to identify topics of texts. Hence, the first step to be realized is the construction of a vocabulary for each topic. Topic vocabularies are composed of words ranked according to their frequencies from the maximum to the minimum. We note that the size of each topic vocabulary is 400. For each word of the vocabulary, average mutual information (AMI) is calculated. The used triggers are selected according to the highest AMI values. In order to evaluate the TR Classifier, we compared it to the well-known TFIDF.