General Topic Annotation in Social Networks: A Latent Dirichlet Allocation Approach

Abstract

In this article, we present a novel document annotation method that can be applied on corpora containing short documents such as social media texts. The method applies Latent Dirichlet Allocation (LDA) on a corpus to initially infer some topical word clusters. Each document is assigned one or more topic clusters automatically. Further document annotation is done through a projection of the topics extracted and assigned by LDA into a set of generic categories. The translation from the topical clusters to the small set of generic categories is done manually. Then the categories are used to automatically annotate the general topics of the documents. It is remarkable that the number of the topical clusters that need to be manually mapped to the general topics is far smaller than the number of postings of a corpus that normally need to be annotated to build training and testing sets manually. We show that the accuracy of the annotation done through this method is about 80% which is comparable with inter-human agreement in similar tasks. Additionally, using the LDA method, the corpus entries are represented by low-dimensional vectors which lead to good classification results. The lower-dimensional representation can be fed into many machine learning algorithms that cannot be applied on the conventional high-dimensional text representation methods.