Keyword Extraction – Comparison of Latent Dirichlet Allocation and Latent Semantic Analysis



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Submitted Apr 27, 2022
Published Jun 13, 2022
10.24018/ejmath.2022.3.3.119

Abstract viewed = 490 times

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The main aim of the present study is to compare the keywords extracted from abstracts and full length text of scientific research papers. In addition to that, here, we compare Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) to identify better performer for keyword extraction. This comparative study is divided into three levels, In the first level, scientific research articles on topics such as Indian Economic growth, GDP, Economic Slowdown etc. were collected and abstracts and full length text was extracted from the sources and pre-processed to remove the words and characters which were not useful to obtain the semantic structures or necessary patterns to make the meaningful corpus. In the second level, the pre-processed data were converted into a bag of words and numerical statistic TF-IDF (Term Frequency – Inverse Document Frequency) is used to assess how relevant a word is to a document in a corpus. In the third level, in order to study the feasibility of the Natural Language Processing (NLP) techniques, Latent Semantic analysis (LSA) and Latent Dirichlet Allocations (LDA) methods were applied over the resultant corpus.

Keywords: Keyword Extraction, Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), Natural language Processing

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