Latent Dirichlet Allocation for Automatic Document Categorization

Abstract

Abstract. In this paper we introduce and evaluate a technique for applying latent Dirichlet allocation to supervised semantic categorization of documents. In our setup, for every category an own collection of topics is assigned, and for a labeled training document only topics from its category are sampled. Thus, compared to the classical LDA that processes the entire corpus in one, we essentially build separate LDA models for each category with the category-specific topics, and then these topic collections are put together to form a unified LDA model. For an unseen document the inferred topic distribution gives an estimation how much the document fits into the category.We use this method for Web document classification. Our key results are 46% decrease in 1-AUC value in classification accuracy over tf.idf with SVM and 43% over the plain LDA baseline with SVM. Using a careful vocabulary selection method and a heuristic which handles the effect that similar topics may arise in distinct categories the improvement is 83% over tf.idf with SVM and 82% over LDA with SVM in 1-AUC.

LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization

“…A study was conducted by Bíró and Szabó [12] using LDA for web documents classification. Instead of estimating the topics of the entire documents in the corpus, they built separate LDA models for every category.…”

“…An alternative way to map the texts into a small number of latent topics using LDA had recently been applied to information retrieval and TC. A study was conducted by Bíró and Szabó [12] using LDA for web documents classification. Instead of estimating the topics of the entire documents in the corpus, they built separate LDA models for every category.…”

Fesztivállátogatók véleményeinek számítógéppel támogatott tematikus modellezése– egy kísérlet eredményei = Computer-aided topic modelling based on festival-goers’ opinions– results of an experiment

“…Balogh az ELTE mesterképzésén íródott szakdolgozatában a kuruc.info romaellenes megnyilvánulásait vizsgálta (BALOGH 2015). Bíró PhD értekezésében a rejtett Dirichlet allokáció dokumentumosztályozási lehetőségeit vizsgálta, az eredeti LDA modellt multi-korpusz (MLDA) és linkalapú LDA modellekké továbbfejlesztve (BÍRÓ 2009). A linkalapú LDA modellt Bíró és társai a webes spamszűrésben alkalmazták, ami a konkurens megoldásokhoz képest valamivel magasabb hatékonyságúnak bizonyult a tesztelés során (BÍRÓ et al 2009a).…”

Recommendation system based on user actions in the social network

“…However, there are various algorithms that can be adapted to your data and trained. To highlight the main topic of the text, a model based on the Latent Dirichlet Allocation (LDA) [10] algorithm was used. The main idea of this algorithm is that each document is considered as a set of topics in a certain proportion.…”