Public Opinion Analysis for the Covid-19 Pandemic Based on Sina Weibo Data

Wang, Feng; Gong, Yunpeng

doi:10.1007/978-3-031-20738-9_109

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…At first, scholars generated event vectors by manually defining or extracting qualitative and quantitative event features from social media. Research [2] uses 33 artificial features to assign values to public opinion events; study [3] made a secondary improvement on the basis of [2], reducing 33 features to 30 features; author in [4] combines Twitter and Weibo, and proposes some characteristics to analyze public opinion based on their commonalities; researcher in [5] combines time features and proposes new event features for rumor analysis. The manually defined event features are very dependent on expert knowledge which usually can only be obtained by observing the influencing factors of one period or even the whole period of the event.…”

Section: A Manually Designing Featuresmentioning

confidence: 99%

“…The Generated Event Vectors 1) Experiment setup: We compare our experimental results with the results of three common models: RoBERTa [24], XLnet [25], Manual designing features [3]. We generate the vectors respectively by the above four models, the first three of which are trained with the blog texts of events, and our proposed NL2ER-Transformer Encoder.…”

Section: Evaluation Of Downstream Reversal Prediction Ability Formentioning

confidence: 99%

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Event Feature Pre-training Model Based on Public Opinion Evolution

Wang¹,

Shu-Ru²,

Xiao-Lan³

et al. 2023

IJACSA

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The comments in the evolution of network public opinion events not only reflect the attitude of netizens towards the event itself, but also are the key basis for mastering the dynamics of public opinion. According to the comment data in the event evolution process, an event feature vector pre-training model NL2ER-Transformer is constructed to realize the realtime automatic extraction of event features. Firstly, a semisupervised multi-label curriculum learning model is proposed to generate comment words, event word vectors, event words, and event sentences, so that a public opinion event is mapped into a sequence similar to vectorized natural language. Secondly, based on the Transformer structure, a training method is proposed to simulate the evolution process of events, so that the event vector generation model can learn the evolution law and the characteristics of reversal events. Finally, the event vectors generated by the presented NL2ER-Transformer model are compared with the event vectors generated by the current mainstream models such as XLNet and RoBerta. This paper tests the pre-trained model NL2ER-Transformer and three pretrained benchmark models on four downstream classification models. The experimental results show that using the vectors generated by NL2ER-Transformer to train downstream models compared to using the vectors generated by other pre-trained benchmark models to train downstream models, the accuracy, recall, and F1 values are 16.66%, 44.44%, and 19% higher than the best downstream model. At the same time, in the evolutionary capability analysis test, only four events show partial errors. In terms of performance of semi-supervised model, the proposed semi-supervised multi-label curriculum learning model outperforms mainstream models in four indicators by 6%, 23%, 8%, and 15%, respectively.

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Section: A Manually Designing Featuresmentioning

confidence: 99%

Section: Evaluation Of Downstream Reversal Prediction Ability Formentioning

confidence: 99%

Event Feature Pre-training Model Based on Public Opinion Evolution

Wang¹,

Shu-Ru²,

Xiao-Lan³

et al. 2023

IJACSA

View full text Add to dashboard Cite

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Machine learning framework for country image analysis

Zúñiga-Morales,

González-Ordiano,

Quiroz-Ibarra

et al. 2024

J Comput Soc Sc

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Innovative ExpTODIM algorithm for interval-valued Pythagorean fuzzy information and new-media information communication influence evaluation

Wang

2024

International Journal of Knowledge-Based and Intelligent Engine

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Undoubtedly, the Internet is an indispensable part of daily life in this era. In this context, the emergence of new media has played a disruptive role in information dissemination, and has also provided new definitions and evaluation standards for the impact of information dissemination. In an environment where excellent research is constantly emerging, it is of research value to redefine the elements of new media information dissemination from a new perspective and provide specific examples to analyze their information dissemination paths. The new-media information communication influence evaluation from super intellectual property (IP) perspective is a multipleattribute group decisionmaking (MAGDM) problem. Recently, the Exponential TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method has been used to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are used as a tool for characterizing uncertain information during the new-media information communication influence evaluation from super intellectual property. In this manuscript, the interval-valued Pythagorean fuzzy Exponential TODIM (ExpTODIM) (IVPFExpTODIM) method is built to solve the MAGDM under IVPFSs. In the end, a numerical case study for new-media information communication influence evaluation from super intellectual property is given to validate the proposed method. The main research contribution of this paper is constructed: (1) the Exponential TODIM (ExpTODIM) method is extended to the IVPFSs; (2) the interval-valued Pythagorean fuzzy Exponential TODIM (ExpTODIM) (IVPF-ExpTODIM) method is built to solve the MAGDM under IVPFSs; (3) the information Entropy is used to obtain the weight values based on the score value and accuracy value under IVPFSs; (4) Finally, a numerical case study for new-media information communication influence evaluation from super intellectual property is given to validate the proposed method.

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Public Opinion Analysis for the Covid-19 Pandemic Based on Sina Weibo Data

Cited by 3 publications

References 11 publications

Event Feature Pre-training Model Based on Public Opinion Evolution

Event Feature Pre-training Model Based on Public Opinion Evolution

Machine learning framework for country image analysis

Innovative ExpTODIM algorithm for interval-valued Pythagorean fuzzy information and new-media information communication influence evaluation

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