Quick Insight of Research Literature Using Topic Modeling

Chakkarwar, V. A.; Tamane, Sharvari

doi:10.1007/978-981-15-0077-0_20

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…However, the studies conducted by Siti Qomariyaha et al in 2019 (26) by using Twitter data as text data were corroborated with our results in this study as they concluded that LDA considers the relationship between documents in the corpus with the best topic coherence than LSA. Also, in comparative studies using different text mining methods as applied to short text data, LDA showed more meaningful extracted topics and obtained good results with topic coherence as an evaluation metric for creating the content of a document collection (6,27) .…”

Section: Discussionmentioning

confidence: 98%

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

Garbhapu¹,

Bodapati²

2020

IJST

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Objective: To compare the topic modeling techniques, as no free lunch theorem states that under a uniform distribution over search problems, all machine learning algorithms perform equally. Hence, here, we compare Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA) to identify better performer for English bible data set which has not been studied yet. Methods: This comparative study divided into three levels: In the first level, bible data was extracted from the sources and preprocessed 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 preprocessed 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, Latent Semantic analysis and Latent Dirichlet Allocations methods were applied over the resultant corpus to study the feasibility of the techniques. Findings: Based on our evaluation, we observed that the LDA achieves 60 to 75% superior performance when compared to LSA using document similarity within-corpus, document similarity with the unseen document. Additionally, LDA showed better coherence score (0.58018) than LSA (0.50395). Moreover, when compared to any word within-corpus, the word association showed better results with LDA. Some words have homonyms based on the context; for example, in the bible; bear has a meaning of punishment and birth. In our study, LDA word association results are almost near to human word associations when compared to LSA. Novelty: LDA was found to be the computationally efficient and interpretable method in adopting the English Bible dataset of New International Version that was not yet created. Keywords: Topic modeling; LSA; LDA; word association; document similarity;Bible data set

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Section: Discussionmentioning

confidence: 98%

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

Garbhapu¹,

Bodapati²

2020

IJST

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“…The LDA introduced by Blei et al ( 2003 ) is a widely used topic model that is considered more complete and an improvement over previous latent semantic allocation (LSA) models (Phan et al 2008 ). It is an unsupervised algorithm within the Bayesian statistical paradigm, which assumes that latent topics exist within the data where each topic is a probability distribution over words (Chakkarwar and Tamane 2020 ; Lyu and Luli 2021 ). Unlike typical clustering (for example, k-means) that assumes a distance measure between clusters and assigns each data point to a particular group, topic modeling produces probabilities of a document belonging to several topics (Imran et al 2015 ; Blum et al 2020 ; Lyu and Luli 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Disaster Impacts Surveillance from Social Media with Topic Modeling and Feature Extraction: Case of Hurricane Harvey

Mihunov

Jafari

Wang

et al. 2022

Int J Disaster Risk Sci

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Twitter can supply useful information on infrastructure impacts to the emergency managers during major disasters, but it is time consuming to filter through many irrelevant tweets. Previous studies have identified the types of messages that can be found on social media during disasters, but few solutions have been proposed to efficiently extract useful ones. We present a framework that can be applied in a timely manner to provide disaster impact information sourced from social media. The framework is tested on a well-studied and data-rich case of Hurricane Harvey. The procedures consist of filtering the raw Twitter data based on keywords, location, and tweet attributes, and then applying the latent Dirichlet allocation (LDA) to separate the tweets from the disaster affected area into categories (topics) useful to emergency managers. The LDA revealed that out of 24 topics found in the data, nine were directly related to disaster impacts—for example, outages, closures, flooded roads, and damaged infrastructure. Features such as frequent hashtags, mentions, URLs, and useful images were then extracted and analyzed. The relevant tweets, along with useful images, were correlated at the county level with flood depth, distributed disaster aid (damage), and population density. Significant correlations were found between the nine relevant topics and population density but not flood depth and damage, suggesting that more research into the suitability of social media data for disaster impacts modeling is needed. The results from this study provide baseline information for such efforts in the future.

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“…A domain of unsupervised machine learning, topic modeling synthesizes unwieldy textual data into more concise and deliverable concepts and organizes them into domains, or topics, based on the patterned clustering of the concepts across a data set [ 28 , 29 ]. In our experiment, topic modeling mined the corpus of clinical notes in the EHRs for common groupings of terms, represented as standardized medical concepts, or CUIs.…”

Section: Methodsmentioning

confidence: 99%

“…Although more recent models and techniques have achieved higher accuracy, LDA is one of the most effective unsupervised probabilistic topic models for text mining based on CUIs. LDA requires a predefined number of topics to model [ 29 ], and coherence value (CV) scores for each subcohort were derived in order to identify the number of topics with the best model fit. Ten topics were determined to be optimal and parsimonious (Figure S1 and Table S1 in Multimedia Appendix 1 ).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study

Thompson

Sharma

Smith

et al. 2022

JMIR Public Health Surveill

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Background The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. Objective We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. Methods This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. Results Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier’s predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). Conclusions Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

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Quick Insight of Research Literature Using Topic Modeling

Cited by 11 publications

References 9 publications

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data

Disaster Impacts Surveillance from Social Media with Topic Modeling and Feature Extraction: Case of Hurricane Harvey

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study

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