Learning Focused Hierarchical Topic Models with Semi-Supervision in Microblogs
Lecture notes in computer sciencePublished 1 January 2015
Anton Slutsky, Xiaohua Hu, Yuan An
Citations2
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
The new Semi-Supervised Microblog-hLDA (SS-Micro-h LDA) model is proposed to discover topic hierarchies in short, noisy microblog documents in a way that allows users to focus topic discovery around interesting areas.
Abstract
Topic modeling approaches, such as Latent Dirichlet Allocation (LDA) and Hierarchical LDA (hLDA) have been used extensively to discover topics in various corpora. Unfortunately, these approaches do not perform well when applied to collections of social media posts. Further, these approaches do not allow users to focus topic discovery around subjectively interesting concepts. We propose the new Semi-Supervised Microblog-hLDA (SS-Micro-hLDA) model to discover topic hierarchies in short, noisy microblog documents in a way that allows users to focus topic discovery around interesting areas. We test SS-Micro-hLDA using a large, public collection of Twitter messages and Reddit social blogging site and show that our model outperforms hLDA, Constrained-hLDA, Recursive-rCRP and TSSB in terms of Pointwise Mutual Information (PMI) Score. Further, we test our model in terms of information entropy of held-out data and show that the new approach produces highly focused topic hierarchies.
Keywords
Computer SciencePhysics and Astronomy
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