![]() in 2003, and thereafter has become the standard for probabilistic text categorization under latent semantic hypotheses. Mallet’s topic modelling is based on the Latent Dirichlet Allocation (LDA) model, a Bayesian probabilistic generative model which has been applied for the first time to text classification tasks by David Blei et al. It allows topic modelling on textual corpora, without requiring advanced technical knowledge in statistics and programming. ![]() Mallet is a tool for topic modelling: it is a Java-based package for statistical natural language processing, which was initially developed by Andrew McCallum at the University of Massachusetts. Moreover, software programs that are executable from command line or user interface have been developed to perform topic modelling, so as to provide more friendly environments for researchers who are not much acquainted with code design. It often represents an appealing approach for data-driven analysis in short-run projects, for it is an unsupervised method, i.e., there is no requirement for algorithm training from labelled data, whose production is quite a demanding task. ![]() Topic modelling aims at extracting topics occurring in a corpus and categorize documents on the basis of their semantic content. ![]() In the context of text mining, topic modelling analyses co-occurrence patterns among textual data, in order to isolate clusters from the set of expressions occurring in a corpus. ![]()
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