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What is Latent Dirichlet Allocation (LDA)

Handbook of Research on Knowledge and Organization Systems in Library and Information Science
LDA is a generative mathematical model that allows a series of results to be explained by unobserved classes that understand why certain parts of the data are identical. For example, if observations are words gathered in documents, it is argued that each text is a combination of a limited number of themes and that the appearance of each word is due to one of the themes of the document. LDA is an example of a theme model which belongs to the machine learning toolbox and, more generally, to the artificial intelligence toolbox.
Published in Chapter:
Predicting the Future Research Gaps Using Hybrid Approach: Machine Learning and Ontology - A Case Study on Biodiversity
Premisha Premananthan (Sabaragamuwa Univeristy of Sri Lanka, Sri Lanka), Banujan Kuhaneswaran (Sabaragamuwa University of Sri Lanka, Sri Lanka), Banage T. G. S. Kumara (Sabaragamuwa Univeristy of Sri Lanka, Sri Lanka), and Enoka P. Kudavidanage (Sabaragamuwa Univeristy of Sri Lanka, Sri Lanka)
DOI: 10.4018/978-1-7998-7258-0.ch009
Sri Lanka is one of the global biodiversity hotspots that contain a large variety of fauna and flora. But nowadays Sri Lankan wildlife has faced many issues because of poor management and policies to protect wildlife. The lack of technical and research support leads many researchers to retreat to select wildlife as their domain of study. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. Then use those results to predict the trending topics of researches in the field of biodiversity. To model topics and extract the main keywords, the authors used the latent dirichlet allocation (LDA) algorithms. Using the topic modeling performance, an ontology model was also developed to describe the relationships between each keyword. They classified the research papers using the artificial neural network (ANN) using ontology instances to predict the future gaps for wildlife research papers. The automatic classification and labeling will lead many researchers to find their desired research papers accurately and quickly.
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Two Case Studies of Online Discussion Use in Computer Science Education: Deep vs. Shallow Integration and Recommendations
A commonly-used topic-modeling algorithm that assumes that a document can be represented as a distribution of topics, and each topic is essentially a collection of phrases with different frequencies.
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