Exploring patient experiences and concerns in the online Cochlear implant community: A cross‐sectional study and validation of automated topic modelling

Williams, Christopher Y.K.; Li, Rosia X; Luo, Michael Y; Eitutis, Susan T

doi:10.1111/coa.14037

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BERTopic, proposed by Grootendorst [25], has progressively gained popularity as a tool for dynamic topic modeling [15], [26], [27]. Figure 1 shows the main components of the BERTopic algorithm.…”

Section: Implementation Of Dynamic Topic Modeling To Discover Topic E...mentioning

confidence: 99%

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Hananto

2023

join

View full text Add to dashboard Cite

Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance. In the last decade, various approaches based on topic modeling have been developed to solve this problem. The known solutions, however, often only work well on content with static topics. As a result, it is challenging to analyze customer reviews that include dynamic and constantly expanding collections of short and noisy texts. A method was proposed to handle such dynamic content. The proposed system applied a dynamic topic model using BERTopic to monitor topics and word evolution over time. It would help decide when the topic model needs to be retrained to capture emerging topics. Several experiments were conducted to test the practicality and effectiveness of the proposed framework. It demonstrated how a dynamic topic model could handle the emergence of new and over-time-correlated topics in customer review data. As a result, improved performance was achieved compared to the baseline static topic model, with 25% of new segmented texts discovered using the dynamic topic model. Experimental results have, therefore, convincingly demonstrated that the proposed framework can be used in practice to develop automatic review annotation tools.

show abstract

Section: Implementation Of Dynamic Topic Modeling To Discover Topic E...mentioning

confidence: 99%

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Hananto

2023

join

View full text Add to dashboard Cite

show abstract

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing

Mapundu,

Kabudula,

Musenge

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mortality, especially in regions lacking robust medical infrastructure. In this study, we propose a comprehensive approach to extract mortality causes and identify prevalent diseases from VA narratives utilizing advanced text mining techniques, so as to better understand the underlying health issues leading to mortality. Our methodology integrates n-gram-based language processing, Latent Dirichlet Allocation (LDA), and BERTopic, offering a multi-faceted analysis to enhance the accuracy and depth of information extraction. This is a retrospective study that uses secondary data analysis. We used data from the Agincourt Health and Demographic Surveillance Site (HDSS), which had 16338 observations collected between 1993 and 2015. Our text mining steps entailed data acquisition, pre-processing, feature extraction, topic segmentation, and discovered knowledge. The results suggest that the HDSS population may have died from mortality causes such as vomiting, chest/stomach pain, fever, coughing, loss of weight, low energy, headache. Additionally, we discovered that the most prevalent diseases entailed human immunodeficiency virus (HIV), tuberculosis (TB), diarrhoea, cancer, neurological disorders, malaria, diabetes, high blood pressure, chronic ailments (kidney, heart, lung, liver), maternal and accident related deaths. This study is relevant in that it avails valuable insights regarding mortality causes and most prevalent diseases using novel text mining approaches. These results can be integrated in the diagnosis pipeline for ease of human annotation and interpretation. As such, this will help with effective informed intervention programmes that can improve primary health care systems and chronic based delivery, thus increasing life expectancy.

show abstract

Exploring patient experiences and concerns in the online Cochlear implant community: A cross‐sectional study and validation of automated topic modelling

Cited by 2 publications

References 26 publications

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing

Contact Info

Product

Resources

About