Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques

Chayangkoon, Narongsak; Srivihok, Anongnart

doi:10.11591/ijece.v11i4.pp3617-3628

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The evaluation indicators of the civil aviation safety risk intelligent early warning model are Confusion, Accuracy, Precision, Recall, F1-Score, and Matthews correlation coefficient (MCC). The calculation formula of each evaluation index 27 is as follows:where TP represents true positive, FP represents false positive, FN represents false negative, and TN represents true negative. 28…”

Section: The Proposed Early Warning Level Identification Methodsmentioning

confidence: 99%

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Civil aviation safety risk intelligent early warning model based on text mining and multi-model fusion

Hou

Xiong

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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Aviation unsafe events often lead to major casualties and property losses. Aviation safety risk intelligent early warning is an important means to ensure the safe and reliable operation of aircraft. Therefore, an intelligent early warning model is urgently needed to quickly predict the risk level and identify potential risks to take targeted measures to realize the active management of safety. To realize the above process, the text mining method is used to extract key risk information from unsafe event reports and input it into the intelligent early warning model to predict its risk level, further constructing the priority processing index to achieve a rapid decision, and finally realize the intelligent safety management process of features extraction to early warning levels identification and then to priority processing. First, domain dictionary and Chinese stop word list are constructed to process the massive text data in the unsafe event’s report. Further, TF-IDF and TextRank are fused to extract key risk information and convert it into feature vectors. Second, the IHT algorithm is used to alleviate the sample class imbalance problem. After that, input the balanced risk information into an improved stacking multi-model fusion algorithm to accurately identify the early warning level and improve the level of active management and control via priority processing index ranking. The effectiveness and feasibility of the proposed method are demonstrated by testing the unsafe event text data of some aircraft maintenance companies and airlines, which promotes the practical application value of text mining technology in the aviation field.

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Section: The Proposed Early Warning Level Identification Methodsmentioning

confidence: 99%

Section: Model Evaluation Indexmentioning

confidence: 99%

Civil aviation safety risk intelligent early warning model based on text mining and multi-model fusion

Hou

Xiong

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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Feature Extraction Methods and Classification for Malware Incident News

Gumilar,

Budiarto,

Galinium

et al. 2023

2023 IEEE International Conference on Cryptography, Informatics, and Cybersecurity (ICoCICs)

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Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach

Azizi,

Jamali,

Spiteri

2024

JMIR Form Res

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Background During the pandemic, patients with dementia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, therefore, identifying posts (formerly tweets) relevant to dementia can be an important support for patients with dementia and their caregivers. However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. Objective The objective of this study was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. Methods We used a combination of natural language processing and ML algorithms with manually annotated posts to identify posts relevant to dementia and COVID-19. We used 3 data sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. Results Our results showed that (pretrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms tested, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an area under the curve of 83.53%. ALBERT substantially outperformed the other algorithms tested, further emphasizing the superior performance of transfer learning algorithms in the classification of posts. Conclusions Transfer learning algorithms such as ALBERT are highly effective in identifying topic-specific posts, even when trained with limited or adjacent data, highlighting their superiority over other ML algorithms and applicability to other studies involving analysis of social media posts. Such an automated approach reduces the workload of manual coding of posts and facilitates their analysis for researchers and policy makers to support patients with dementia and their caregivers and other vulnerable populations.

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Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques

Cited by 4 publications

References 18 publications

Civil aviation safety risk intelligent early warning model based on text mining and multi-model fusion

Civil aviation safety risk intelligent early warning model based on text mining and multi-model fusion

Feature Extraction Methods and Classification for Malware Incident News

Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach

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