The Instagram/Facebook ban on graphic self‐harm imagery: A sentiment analysis and topic modeling approach

2021 ◽  
Author(s):  
Hayden Smith ◽  
William Cipolli
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling ◽  
Modeling Approach ◽  
Self Harm

Analyzing Brexit’s impact using sentiment analysis and topic modeling on Twitter discussion

2020 ◽  
Author(s):  
Sardar Haider Waseem Ilyas ◽  
Zainab Tariq Soomro ◽  
Ahmed Anwar ◽  
Hamza Shahzad ◽  
Ussama Yaqub
Keyword(s):  
Sentiment Analysis ◽  
Topic Modeling

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.

scholarly journals Understanding the nature and scope of clinical research commentaries in PubMed

2019 ◽  
Vol 27 (3) ◽  
pp. 449-456
Author(s):  
James R Rogers ◽  
Hollis Mills ◽  
Lisa V Grossman ◽  
Andrew Goldstein ◽  
Chunhua Weng
Keyword(s):  
Clinical Research ◽  
Sentiment Analysis ◽  
Random Sample ◽  
Full Text ◽  
Topic Modeling ◽  
Critical Appraisal ◽  
Research Articles ◽  
Disease Specific ◽  
Research Studies

Abstract Scientific commentaries are expected to play an important role in evidence appraisal, but it is unknown whether this expectation has been fulfilled. This study aims to better understand the role of scientific commentary in evidence appraisal. We queried PubMed for all clinical research articles with accompanying comments and extracted corresponding metadata. Five percent of clinical research studies (N = 130 629) received postpublication comments (N = 171 556), resulting in 178 882 comment–article pairings, with 90% published in the same journal. We obtained 5197 full-text comments for topic modeling and exploratory sentiment analysis. Topics were generally disease specific with only a few topics relevant to the appraisal of studies, which were highly prevalent in letters. Of a random sample of 518 full-text comments, 67% had a supportive tone. Based on our results, published commentary, with the exception of letters, most often highlight or endorse previous publications rather than serve as a prominent mechanism for critical appraisal.

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

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.

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