An Empirical Study of TextRank for Keyword Extraction

Zhang, Mingxi; Li, Xuemin; Yue, Shuibo; Yang, Lizi

doi:10.1109/access.2020.3027567

Cited by 36 publications

(11 citation statements)

References 41 publications

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“…Pre-processing of input data is one of the important tasks to transform the unorganized data into a structural format [36]. It aims to improve the quality of the input data for models to better understand the patterns and extract distinctive features from the input data.…”

Section: B Pre-processingmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques. Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist health-care professionals and companies for evaluating the safety of drugs after it has been marketed. Such insights help safeguard patients and increase their trust in medical companies. The existing systems either follow a lexicon-based approach or a learningbased approach for sentiment analysis in the medical domain. Learning-based techniques require annotated data while lexicon-based techniques tend to be domain-specific which restricts their wide use. This research embarks on a hybrid technique that utilizes both learning-based and lexicon-based approaches to achieve better results. General-purpose sentiment lexicons, such as AFFIN, TextBlob, and VADER, are used for annotating the reviews. Furthermore, several feature engineering techniques, such as term frequency (TF), term frequency-inverse document frequency (TF-IDF), and union of TF and TF-IDF (TF U TF-IDF) have been incorporated for the extraction of useful features. Finally, the learning models including logistic regression (LR), AdaBoost classifier (AB), random forest (RF), extra tree classifier (ETC), and multilayer perceptron (MLP) are used to classify sentiments of the reviews. The performance of the proposed hybrid approach is evaluated using accuracy, precision, recall, and F1-score. Experimental results indicate that the combination of learning-based and lexicon-based approaches provide improved results than their individual use. Moreover, TextBlob has shown promising results giving an accuracy of 96% with MLP when used with TF-IDF and with LR when used with TF U TF-IDF.

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Section: B Pre-processingmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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“… • PS (Porter Stemmer) was used to index each news page/article to filter out any stop, repeated, and common words to avoid noise in the dataset. The algorithm was used, over several rounds, to remove any non-relevant words from the datasets/textual scripts before considering all criteria or defined rules (Zhang et al, 2020 ). Such an algorithm has been proven to be one of the best techniques in terms of performance (Joshi et al, 2016 ).…”

Section: Implementation Procedures Findings and Proposed Modelmentioning

confidence: 99%

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

et al. 2022

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Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

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“…With the emergence of word vector technology that converts words into numerical vectors, word meaning measurement becomes possible. The main derived word vector generation models include Word2vec [15], G1oVe [16], ELMo [17], and BERT [18]. The most commonly used are the Word2vec model and the BERT model.…”

Section: The Research Statusmentioning

confidence: 99%

Hot Keyword Extraction of Sci-tech Periodicals Based on the Improved BERT Model

2022

KSII TIIS

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With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

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An Empirical Study of TextRank for Keyword Extraction

Cited by 36 publications

References 41 publications

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Hot Keyword Extraction of Sci-tech Periodicals Based on the Improved BERT Model

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