Unveiling Insights: A Knowledge Discovery Approach to Comparing Topic Modeling Techniques in Digital Health Research
Abstract
This paper introduces a knowledge discovery approach focused on comparing topic modeling techniques within the realm of digital health research. Knowledge discovery has been applied in massive data repositories (databases) and also in various field studies, which use these techniques for finding patterns in the data, determining which models and parameters might be suitable, and looking for patterns of interest in a specific representational. Unfortunately, the investigation delves into the utilization of Latent Dirichlet Allocation (LDA) and Pachinko Allocation Models (PAM) as generative probabilistic models in knowledge discovery, which is still limited. The study's findings position PAM as the superior technique, showcasing the greatest number of distinctive tokens per topic and the fastest processing time. Notably, PAM identifies 87 unique tokens across 10 topics, surpassing LDA Gensim's identification of only 27 unique tokens. Furthermore, PAM demonstrates remarkable efficiency by swiftly processing 404 documents within an incredibly short span of 0.000118970870 seconds, in contrast to LDA Gensim's considerably longer processing time of 0.368770837783 seconds. Ultimately, PAM emerges as the optimum method for digital health research's topic modeling, boasting unmatched efficiency in analyzing extensive digital health text data.
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References
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