scholarly journals What Does Personality Mean in the Context of Mental Health? A Topic Modeling Approach Based on Abstracts Published in Pubmed Over the Last 5 Years

2020 ◽  
Vol 10 ◽  
Author(s):  
Raffaele Sperandeo ◽  
Giovanni Messina ◽  
Daniela Iennaco ◽  
Francesco Sessa ◽  
Vincenzo Russo ◽  
...  
Keyword(s):  
Mental Health ◽  
Topic Modeling ◽  
Modeling Approach

scholarly journals Modeling Human Factors Topics in Aviation Reports

2019 ◽  
Vol 63 (1) ◽  
pp. 126-130
Author(s):  
Beth Lyall-Wilson ◽  
Nicolas Kim ◽  
Elizabeth Hohman
Keyword(s):  
Human Factors ◽  
Topic Modeling ◽  
Domain Knowledge ◽  
Aviation Safety ◽  
Subject Matter Experts ◽  
Text Data ◽  
Modeling Approach ◽  
Modeling Process ◽  
Manual Review ◽  
Initial Extraction

This paper describes the development and new application of a text modeling process for identifying human factors topics, such as fatigue, workload, and distraction in aviation safety reports. Current approaches to identifying human factors topic representations in text data rely on manual review from subject matter experts. The implementation of a semi-supervised text modeling method overcomes the need for lengthy manual review through an initial extraction of pre-defined human factors topics, freeing time for focus on analyzing the information. This modeling approach allows analysts to use keywords to define topics of interest up front and influence the convergence of the model toward a result that reflects them, which provides an advantage over classic topic modeling approaches where domain knowledge is not integrated into the generation of derived topics. This paper includes a description of the modeling approach and rationale, data used, evaluation methods, challenges, and suggestions for future applications.

An Evaluation of Patient Safety Event Report Categories Using Unsupervised Topic Modeling

2015 ◽  
Vol 54 (04) ◽  
pp. 338-345 ◽  
Author(s):  
A. Fong ◽  
R. Ratwani
Keyword(s):  
Patient Safety ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Topic Modeling ◽  
Free Text ◽  
Event Data ◽  
Event Type ◽  
Modeling Approach ◽  
Safety Event

SummaryObjective: Patient safety event data repositories have the potential to dramatically improve safety if analyzed and leveraged appropriately. These safety event reports often consist of both structured data, such as general event type categories, and unstructured data, such as free text descriptions of the event. Analyzing these data, particularly the rich free text narratives, can be challenging, especially with tens of thousands of reports. To overcome the resource intensive manual review process of the free text descriptions, we demonstrate the effectiveness of using an unsupervised natural language processing approach.Methods: An unsupervised natural language processing technique, called topic modeling, was applied to a large repository of patient safety event data to identify topics, or themes, from the free text descriptions of the data. Entropy measures were used to evaluate and compare these topics to the general event type categories that were originally assigned by the event reporter.Results: Measures of entropy demonstrated that some topics generated from the un-supervised modeling approach aligned with the clinical general event type categories that were originally selected by the individual entering the report. Importantly, several new latent topics emerged that were not originally identified. The new topics provide additional insights into the patient safety event data that would not otherwise easily be detected.Conclusion: The topic modeling approach provides a method to identify topics or themes that may not be immediately apparent and has the potential to allow for automatic reclassification of events that are ambiguously classified by the event reporter.

Drawing openness to experience from user generated contents: An interpretable data-driven topic modeling approach

2020 ◽  
Vol 144 ◽  
pp. 113073 ◽  
Author(s):  
Yishi Zhang ◽  
Haiying Wei ◽  
Yaxuan Ran ◽  
Yang Deng ◽  
Dan Liu
Keyword(s):  
Topic Modeling ◽  
Openness To Experience ◽  
Data Driven ◽  
Modeling Approach

scholarly journals On Building an Interpretable Topic Modeling Approach for the Urdu Language

2020 ◽  
Author(s):  
Zarmeen Nasim
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Topic Modeling ◽  
Language Modeling ◽  
Low Resource ◽  
Modeling Approach ◽  
Part Of Speech ◽  
Modeling Techniques ◽  
Pos Tagger

This research is an endeavor to combine deep-learning-based language modeling with classical topic modeling techniques to produce interpretable topics for a given set of documents in Urdu, a low resource language. The existing topic modeling techniques produce a collection of words, often un-interpretable, as suggested topics without integrat-ing them into a semantically correct phrase/sentence. The proposed approach would first build an accurate Part of Speech (POS) tagger for the Urdu Language using a publicly available corpus of many million sentences. Using semanti-cally rich feature extraction approaches including Word2Vec and BERT, the proposed approach, in the next step, would experiment with different clus-tering and topic modeling techniques to produce a list of potential topics for a given set of documents. Finally, this list of topics would be sent to a labeler module to produce syntactically correct phrases that will represent interpretable topics.

scholarly journals Topic Discovery of Online Course Reviews Using LDA with Leveraging Reviews Helpfulness

2019 ◽  
Vol 9 (1) ◽  
pp. 426
Author(s):  
Fetty Fitriyanti Lubis ◽  
Yusep Rosmansyah ◽  
Suhono H. Supangkat
Keyword(s):  
Topic Modeling ◽  
Online Courses ◽  
Influential Factors ◽  
Massive Open Online Courses ◽  
Online Course ◽  
Small Scale ◽  
Massive Open Online ◽  
Topic Discovery ◽  
Modeling Approach ◽  
Teaching Learning

Despite the popularity of the Massive Open Online Courses, small-scale research has been done to understand the factors that influence the teaching-learning process through the massive online platform. Using topic modeling approach, our results show terms with prior knowledge to understand e.g.: Chuck as the instructor name. So, we proposed the topic modeling approach on helpful subjective reviews. The results show five influential factors: “learn easy excellent class program”, “python learn class easy lot”, “Program learn easy python time game”, and “learn class python time game”. Also, research results showed that the proposed method improved the perplexity score on the LDA model.

scholarly journals The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

2021 ◽  
Author(s):  
Senqi Zhang ◽  
Li Sun ◽  
Daiwei Zhang ◽  
Pin Li ◽  
Yue Liu ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Topic Modeling ◽  
The United States ◽  
Allocation Model ◽  
Health Concerns ◽  
Demographic Composition ◽  
The Us ◽  
Mental Health Concerns ◽  
Twitter Users

AbstractBackgroundMental health illness is a growing problem in recent years. During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media.ObjectiveIn this study, we aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the United States and infer the demographic composition of Twitter users who had mental health concerns.MethodsCOVID-19 related tweets from March 5th, 2020 to January 31st, 2021 were collected through Twitter streaming API using COVID-19 related keywords (e.g., “corona”, “covid19”, “covid”). By further filtering using mental health keywords (e.g., “depress”, “failure”, “hopeless”), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users’ discussions surrounding mental health concerns. Demographic inference using deep learning algorithms (including Face++ and Ethnicolr) was performed to infer the demographic composition of Twitter users who had mental health concerns during the COVID-19 pandemic.ResultsWe observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that “stay-at-home”, “death poll” and “politics and policy” were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.ConclusionsThe COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males, White) were more likely to have mental health concerns during the COVID-19 pandemic.

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