Using lda2vec Topic Modeling to Identify Latent Topics in Aviation Safety Reports

Luo, Yonghui; Si, Hongwei

doi:10.1109/icis46139.2019.8940271

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…An LDA model was then used to map the latent semantic space, forming the document topic feature vectors of narrative text in a report. The approach yielded a marginally higher coherence score than LDA alone across a number of topics ranging from 1-20 [56].…”

Section: Topic Modellingmentioning

confidence: 95%

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A Scoping Literature Review of Natural Language Processing Application to Safety Occurrence Reports

Ricketts

Barry

Guo

et al. 2023

Safety

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Safety occurrence reports can contain valuable information on how incidents occur, revealing knowledge that can assist safety practitioners. This paper presents and discusses a literature review exploring how Natural Language Processing (NLP) has been applied to occurrence reports within safety-critical industries, informing further research on the topic and highlighting common challenges. Some of the uses of NLP include the ability for occurrence reports to be automatically classified against categories, and entities such as causes and consequences to be extracted from the text as well as the semantic searching of occurrence databases. The review revealed that machine learning models form the dominant method when applying NLP, although rule-based algorithms still provide a viable option for some entity extraction tasks. Recent advances in deep learning models such as Bidirectional Transformers for Language Understanding are now achieving a high accuracy while eliminating the need to substantially pre-process text. The construction of safety-themed datasets would be of benefit for the application of NLP to occurrence reporting, as this would allow the fine-tuning of current language models to safety tasks. An interesting approach is the use of topic modelling, which represents a shift away from the prescriptive classification taxonomies, splitting data into “topics”. Where many papers focus on the computational accuracy of models, they would also benefit from real-world trials to further inform usefulness. It is anticipated that NLP will soon become a mainstream tool used by safety practitioners to efficiently process and gain knowledge from safety-related text.

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Section: Topic Modellingmentioning

confidence: 95%

“…A different form of topic modelling has been conducted whereby feature word vectors of narrative text were obtained via Word2Vec training [56]. An LDA model was then used to map the latent semantic space, forming the document topic feature vectors of narrative text in a report.…”

Section: Topic Modellingmentioning

confidence: 99%

A Scoping Literature Review of Natural Language Processing Application to Safety Occurrence Reports

Ricketts

Barry

Guo

et al. 2023

Safety

View full text Add to dashboard Cite

show abstract

“…Zhou et al [16] build the physical architecture of ATS and create the groundwork for application. [20] collect information in aviation safety reports and successfully complete the mining latent topics task. In [21][22][23], the topic model is used in diferent languages and gets the same satisfactory results.…”

Section: Autonomous Transportation System (Ats)mentioning

confidence: 99%

Intergeneration Division Based on Key Component Analysis in an Autonomous Transportation System Using the Natural Language Processing Method

Gou

Xiong

2023

Journal of Advanced Transportation

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Advancement of emerging technologies and increasing of transport demands accelerate the evolution of the autonomous transportation system (ATS). Framework and architecture of ATS are becoming a research hotspot; however, by far, few studies on transportation intergeneration division are not basically involved. Previous works indicate that key components are critical representation in the distinguishing of long-term era. Besides, massive text material accumulates as the research work goes on, and natural language processing technique keeps developing, which makes quantitative research on key components in intergeneration division become possible. In this work, a method based on the massive text analysis is proposed. First, the LDA2vec is used to get the relationship between components and other elements. Then, a word set is from the component word set extraction module based on component items. Finally, the component word set is clustered to get ATS generation and to generate key components. Based on an analysis of large-scale important traffic texts, our method divides the traffic system into three generations for Chinese traffic from 2010 to 2022. The key components of our method given are consistent with human cognition of ATS. Successful application indicates that this work can be extended to other intergeneration division fields.

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“…According to the chart, with the number of topics k = 5 for positive themes and k = 4 for negative themes, the topic consistency score is the highest. As a result, the number of topics k chosen for positive and negative themes in this experiment is 5, 4 consecutively to conduct LDA topic modelling for text data from mHealth applications (Luo & Shi, 2019).…”

Section: Thematic Analysis Of Positive and Negative Reviewsmentioning

confidence: 99%

Unsupervised Machine Learning to Identify Positive and Negative Themes in Jordanian mHealth Apps

Alhur

Alshamari

Oláh

et al. 2022

International Journal of E-Services and Mobile Applications

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User opinions are crucial in the development of mobile health (mHealth) applications (apps). This study aimed to investigate and qualitatively assess consumer attitudes toward mHealth apps and the main aspects of their design. The methodology was divided into four steps: (1) data collection, (2) preprocessing, (3) sentiment analysis by valence-aware dictionary and sentiment reasoner (VADER), and (4) thematic analysis by the latent Dirichlet allocation (LDA) algorithm. These steps were implemented in 836 reviews of eight mHealth apps on app stores in Jordan. The current study offers healthcare stakeholders insight into the positive and negative aspects of mHealth apps by identifying user-preferred features and recommending improvements. The findings indicate several aspects of design that mHealth app developers may use to improve overall efficacy, including user experience, client services, usability, and adherence.

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Using lda2vec Topic Modeling to Identify Latent Topics in Aviation Safety Reports

Cited by 10 publications

References 7 publications

A Scoping Literature Review of Natural Language Processing Application to Safety Occurrence Reports

A Scoping Literature Review of Natural Language Processing Application to Safety Occurrence Reports

Intergeneration Division Based on Key Component Analysis in an Autonomous Transportation System Using the Natural Language Processing Method

Unsupervised Machine Learning to Identify Positive and Negative Themes in Jordanian mHealth Apps

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