A trend analysis of machine learning research with topic models and Mann-Kendall test

A trend analysis of machine learning research with topic models and Mann-Kendall test

Автор: Deepak Sharma, Bijendra Kumar, Satish Chand

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 2 vol.11, 2019 года.

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This paper aims to systematically examine the literature of machine learning for the period of 1968~2017 to identify and analyze the research trends. A list of journals from well-established publishers ScienceDirect, Springer, JMLR, IEEE (approximately 23,365 journal articles) related to machine learning is used to prepare a content collection. To the best of our information, it is the first effort to comprehend the trend analysis in machine learning research with topic models: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and LDA with Coherent Model (LDA_CM). The LDA_CM topic model gives the highest topic coherence amongst all topic models under consideration. This study provides a scientific ground that helps to overcome the subjectivity of collective opinion. The Mann-Kendall test is used to understand the trend of the topics. Our findings provide indicative of paradigmatic shifts in research methodology of significant patterns of topical prominence and the evolving research areas. It is used to highlight the evolution regarding the previous and recent trends in research topics in the area of machine learning. Understanding such an intellectual structure and future trends will assist the researchers to adopt the divergent developments of this research in one place. This paper analyzes the overall trends of the machine learning research since 1968, based on the latent topics identified in the period of 2007~2017 that may be helpful to the researchers exploring the recommended areas and publish their research articles.

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Latent Semantic Analysis, Latent Dirichlet Allocation, Coherence Model, Text Mining, Data Mining, Machine Learning, Trend Analysis

Короткий адрес: https://sciup.org/15016574

IDR: 15016574   |   DOI: 10.5815/ijisa.2019.02.08

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