Optimized Dynamic Latent Topic Model For Big Text Data Analytics

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JOMO KENYATTA UNIVERSITY
OF
AGRICULTURE AND TECHNOLOGY

SCHOOL OF COMPUTING AND INFORMATION TECHNOLOGY

Optimized Dynamic Latent Topic Model for Big Text Data Analytics

NAME: Geoffrey Mariga Wambugu
REGISTRATION NUMBER: CS481-4692/2014

LECTURER: Prof. Waweru Mwangi

A thesis proposal submitted in partial fulfilment of the requirement for the Unit SCI 4201 Advanced Research Methodology of the degree of Doctor of Philosophy in Information Technology at the School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology

June 2015
Abstract
Probabilistic topic modeling provides computational methods for large text data analysis. Today streaming text mining plays an important role within real-time social media mining. Latent Dirichlet Allocation (LDA) model was developed a decade ago to aid discovery of the hidden thematic structure in large archives of documents. It is acknowledged by many researchers as the most popular approach for building topic models. In this study, we discuss topic modeling and more specifically LDA. We identify speed as one of the major limitations of LDA application in streaming big text data analytics. The main aim of this study is to enhance inference speed of LDA thereby develop a new inference method and algorithm. Given the characteristics of this specific research problem, the approach to the proposed research will follow the experimental model. We will investigate causal relationships using a test

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