On Mining Words: The Utility of Topic Models in Health Education Research and Practice

Abstract

Written language is the primary means by which scientific research findings are disseminated. Yet in the era of information overload, dissemination of a field of research may require additional efforts given the sheer volume of material available on any specific topic. Topic models are unsupervised natural language processing methods that analyze nonnumeric data (i.e., text data) in abundance. These tools aggregate, and make sense of, those data making them interpretable to interested audiences. In this perspective piece, we briefly describe topic models, including their purpose, function, and applicability for health education researchers and practitioners. We note how topic models can be applied in several contexts, including social media–based analyses, and mapping trends in scientific literature over time. As a tool for studying words, and patterns of words, topic models stand to improve our understanding of events prior and those occurring in the moment and help us look ahead into the future.

Identifying the knowledge structure of electromagnetic fields and health research: Text network analysis and topic modeling

“…Latent Dirichlet allocation (LDA) is the most frequently used algorithm in topic modeling that learns a set of topics from words that tend to occur together in documents [ 15 ]. It identifies hidden topics within documents and document sets and uncovers the ratio of topics for each document and the probability of each word being included in each topic [ 17 ].…”

“…Latent Dirichlet allocation (LDA) is the most frequently used algorithm in topic modeling that learns a set of topics from words that tend to occur together in documents [15]. It identifies…”

“…Therefore, TNA can be used to identify knowledge structures and research topic trends based on the frequency, centrality, and co-occurrence of keywords [ 14 ]. Knowledge structure analysis using text networks quantitatively derives the key concepts of a particular field and helps visualize relationships between them [ 15 ]. By identifying the knowledge structure of EMFs and health research, we can identify research trends and suggest future research directions.…”

Neutral or Framed? A Sentiment Analysis of 2019 Abortion Laws

“…As demonstrated in our study, sentiment analysis as an analytical tool can be useful to diagnose polarization of legal documents, alongside the extent to which the documents' texts skew positively or negatively. Nonetheless, as with all NLP analyses, additional context is needed to interpret these outcomes and associated implications (Valdez et al, 2021). Abortion Bills' Framing: Context(s)…”

Identifying the Knowledge Structure and Trends of Outreach in Public Health Care: A Text Network Analysis and Topic Modeling

“…Since there is a huge amount of data available on any specific topic, topic modeling was performed to understand the topic of the research field. It is an unsupervised natural language processing method that analyzes non-numeric data such as text data in abundance, and aggregates and understands those data making them interpretable to interested audiences [26]. For our topic modeling, we performed Latent Dirichlet Allocation (LDA) analysis, whose algorithm is the most popular and frequently used among other topic modeling methods [27][28][29].…”