Comparison of Text Mining Feature Extraction Methods Using Moderated vs Non-Moderated Blogs

Tayeen, Abu Saleh Md; Masadeh, Saleem; Mtibaa, Abderrahmen; Misra, Satyajayant; Choudhury, Moumita

doi:10.1145/3357729.3357740

Cited by 1 publication

(2 citation statements)

References 25 publications

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“…Furthermore, feature engineering is a labor-intensive and time-consuming process [40]. Recently, feature extraction models have been proposed widely to enhance the analysis of unstructured data incorporated in text documents [41]. We primarily concentrate on token-level feature extraction because this work involves diagnosis tasks.…”

Section: Feature Extractionmentioning

confidence: 99%

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Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Mahdi¹,

Yuhaniz²

2023

Computers, Materials &Amp; Continua

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The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease. Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems. In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease. Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS). However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data. In this research, we proposed a modification term weighting scheme: RTF-C-IEF and compare the proposed model with four extraction methods: TF, TF-IDF, TF-IHF, and TF-IEF. The experiment was conducted on two new datasets for COVID-19 patients. The first dataset was collected from government hospitals in Iraq with 3053 clinical records, and the second dataset with 1446 clinical reports, was collected from several different websites. Based on the experimental results using several popular classifiers applied to the datasets of Covid-19, we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments. Further, the modified RTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments. Thus, the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term.

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Section: Feature Extractionmentioning

confidence: 99%

“…It defined using Eqs. ( 6) and (7), according to the True Positives (TP), the False Positives (FP), and the False Negatives (FN) conclusions [41] . In this study, accuracy defined as the percentage of patients that are properly classified as infected with Covid-19 and "non-Covid-19 infected" as follows:…”

Section: Evaluation Criteriamentioning

confidence: 99%

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Mahdi¹,

Yuhaniz²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Comparison of Text Mining Feature Extraction Methods Using Moderated vs Non-Moderated Blogs

Cited by 1 publication

References 25 publications

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

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