Deep LDA : A new way to topic model. Issue 3 (2nd April 2020)

• Record Type: Journal Article
• Authors: Bhat, Muzafar Rasool; Kundroo, Majid A; Tarray, Tanveer A; Agarwal, Basant
• Is Part Of: Journal of information & optimization sciences. Volume 41:Issue 3(2020)
• Page Start: 823
• Page End: 834
• Publication Date: 2020-04-02
• Subjects: Deep Learning -- LDA -- SVM Classifier -- Topic Modelling -- Keras -- Tensorflow; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2019.1616911
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: Probabilistic topic models like Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM) have been successfully implemented and used in many areas like movie reviews, recommender systems and text summarization etc. These models however become computationally heavy if tested on humongous corpus. Keeping in view the vide acceptability of Deep Neural network based machine learning, this research proposes two deep neural network variants (2NN DeepLDA and 3NN DeepLDA) of existing topic modeling technique Latent Dirichlet Allocation (LDA) with specific aim to handle large corpuses with less computational efforts. Two proposed models (2NN DeepLDA and 3NN DeepLDA) are used to mimic the statistical process of Latent Dirichlet Allocation. Reuters-21578 dataset has been used in the study. Results computed from LDA are compared with the proposed models (2NN DeepLDA and 3NN DeepLDA) using Support Vector Machine (SVM) classifier. Proposed models have shown significant accuracy besides computational effectiveness in comparison to traditional LDA.