Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

Zhu, Yanan; Li, Luyao; Li, Chenguang; Zhang, Wei

doi:10.56578/ocs020105

OCS

2023

DOI: 10.56578/ocs020105

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Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

Yanan Zhu¹,

Luyao Li²,

Chenguang Li³

et al.

Abstract: As a major public health emergency, the COVID-19 pandemic has some uncertainties. Coupling of the uncertainties and anti-epidemic policies easily leads to the spread of negative emotions. It is challenging to maintain the sustainability of anti-epidemic measures. Therefore, this paper aimed to analyze the challenges that the sustainability of anti-epidemic measures is faced with. A topic clustering extension method was proposed, which integrated Latent Dirichlet Allocation (LDA) topic information with Bidirect… Show more

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Cited by 2 publications

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O$$^2$$-Bert: Two-Stage Target-Based Sentiment Analysis

Yan,

Zhang,

Ding

et al. 2023

Cogn Comput

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Target-based sentiment analysis (TBSA) is one of the most important NLP research topics for widespread applications. However, the task is challenging, especially when the targets contain multiple words or do not exist in the sequences. Conventional approaches cannot accurately extract the (target, sentiment) pairs due to the limitations of the fixed end-to-end architecture design. In this paper, we propose a framework named O$$^2$$ 2 -Bert, which consists of Opinion target extraction (OTE-Bert) and Opinion sentiment classification (OSC-Bert) to complete the task in two stages. More specifically, we divide the OTE-Bert into three modules. First, an entity number prediction module predicts the number of entities in a sequence, even in an extreme situation where no entities are contained. Afterwards, with predicted number of entities, an entity starting annotation module is responsible for predicting their starting positions. Finally, an entity length prediction module predicts the lengths of these entities, and thus, accomplishes target extraction. In OSC-Bert, the sentiment polarities of extracted targets from OTE-Bert. According to the characteristics of BERT encoders, our framework can be adapted to short English sequences without domain limitations. For other languages, our approach might work through altering the tokenization. Experimental results on the SemEval 2014-16 benchmarks show that the proposed model achieves competitive performances on both domains (restaurants and laptops) and both tasks (target extraction and sentiment classification), with F1-score as evaluated metrics. Specifically, OTE-Bert achieves 84.63%, 89.20%, 83.16%, and 86.88% F1 scores for target extraction, while OSC-Bert achieves 82.90%, 80.73%, 76.94%, and 83.58% F1 scores for sentiment classification, on the chosen benchmarks. The statistics validate the effectiveness and robustness of our approach and the new “two-stage paradigm”. In future work, we will explore more possibilities of the new paradigm on other NLP tasks.

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O$$^2$$-Bert: Two-Stage Target-Based Sentiment Analysis

Yan,

Zhang,

Ding

et al. 2023

Cogn Comput

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Modeling emotional contagion in the COVID-19 pandemic: a complex network approach

Zhu,

Zhang,

2023

PeerJ Computer Science

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During public health crises, the investigation into the modes of public emotional contagion assumes paramount theoretical importance and has significant implications for refining epidemic strategies. Prior research predominantly emphasized the antecedents and aftermath of emotions, especially those of a negative nature. The interplay between positive and negative emotions, as well as their role in the propagation of emotional contagion, remains largely unexplored. In response to this gap, an emotional contagion model was developed, built upon the foundational model and enriched from a complex network standpoint by integrating a degradation rate index. Stability analyses of this model were subsequently conducted. Drawing inspiration from topological structural features, an enhanced model was introduced, anchored in complex network principles. This enhanced model was then experimentally assessed using Watts-Strogatz’s small-world network, Barabási-Albert’s scale-free network, and Sina Weibo network frameworks. Results revealed that the rate of infection predominantly dictates the velocity of emotional contagion. The incitement rate and purification rate determine the overarching direction of emotional contagion, whereas the degradation rate modulates the waning pace of emotions during intermediate and later stages. Furthermore, the immunity rate was observed to influence the proportion of each state at equilibrium. It was discerned that a greater number of initial emotional disseminators, combined with a larger initial contagion node degree, can amplify the emotion contagion rate across the social network, thus augmenting both the peak and overall influence of the contagion.

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Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

Cited by 2 publications

References 15 publications

O$$^2$$-Bert: Two-Stage Target-Based Sentiment Analysis

O$$^2$$-Bert: Two-Stage Target-Based Sentiment Analysis

Modeling emotional contagion in the COVID-19 pandemic: a complex network approach

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