CNN-LSTM neural network model for fine-grained negative emotion computing in emergencies

Zhang, Wei; Li, Luyao; Zhu, Yanchun; Yu, Peng; Wen, Jianbo

doi:10.1016/j.aej.2021.12.022

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…To assess the performance of the proposed model, we conducted comparative experiments using eight deep learning models with similar architectures [19,23,[31][32][33][34][35][36].…”

Section: Model Comparisonmentioning

confidence: 99%

“…As a result, the proposed model outperforms other deep learning models. [23] 0.9125 0.8629 0.8622 ATT+BiLSTM [31] 0.8976 0.8397 0.8803 CNN [32] 0.8966 0.8384 0.8546 LSTM+CNN [33] 0.8791 0.8103 0.8671 ATT+LSTM+CNN [34] 0.9016 0.8503 0.8869 CNN+BiLSTM [35] 0.9073 0.8555 0.8772 CNN+BiGRU [36] 0 The confusion matrix in Figure 11 reveals the prediction accuracy of positive, neutral, and negative labels in the test set were 87.42%, 90.30%, and 95.83%, respectively. Notably, the accuracy of neutral and negative emotions exceeded 90%, indicating that this model is effective at multi-class sentiment analysis.…”

Section: Model Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

A Novel AB-CNN Model for Multi-Classification Sentiment Analysis of e-Commerce Comments

Zhu

et al. 2023

Electronics

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Despite the success of dichotomous sentiment analysis, it does not encompass the various emotional colors of users in reality, which can be more plentiful than a mere positive or negative association. Moreover, the complexity and imbalanced nature of Chinese text presents a formidable obstacle to overcome. To address prior inadequacies, the three-classification method is employed and a novel AB-CNN model is proposed, incorporating an attention mechanism, BiLSTM, and a CNN. The proposed model was tested on a public e-commerce dataset and demonstrated a superior performance compared to existing classifiers. It utilizes a word vector model to extract features from sentences and vectorize them. The attention layer is used to calculate the weighted average attention of each text, and the relevant representation is obtained. BiLSTM is then employed to read the text information from both directions, further enhancing the emotional level. Finally, softmax is used to classify the emotional polarity.

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“…To assess the performance of the proposed model, we conducted comparative experiments using eight deep learning models with similar architectures [19,23,[31][32][33][34][35][36].…”

Section: Model Comparisonmentioning

confidence: 99%

Section: Model Comparisonmentioning

confidence: 99%

A Novel AB-CNN Model for Multi-Classification Sentiment Analysis of e-Commerce Comments

Zhu

et al. 2023

Electronics

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“…Online emotions is a collection of similar emotional experiences that a certain number of internet users share about an event or a social phenomenon within a certain time frame. Current studies on online emotions focus on two aspects: (1) the classification of different dimensions of online emotions; and (2) the formation factors of online emotions and their effects (Zhang et al, 2022a). For the research on online emotions in public health emergencyies, most scholars have focused on a specific epidemic, identified the overall emotional states and characteristics of netizens using social media data, and explored and identified the role of the epidemic in influencing the emotions of netizens, such as the aggravation of stress, anxiety, depression, panic and other emotions, as well as the decrease in well-being of life (Ahmad & Murad, 2020;Lin et al, 2020).…”

Section: Online Emotions and Impactmentioning

confidence: 99%

“…However, few studies have focused on the effects of anti-epidemic policies and adjustments on netizens' emotions, such as delay of school opening, lockdown, etc. (Wen & Zheng, 2022;Zhang et al, 2022a). Sukhwal & Kankanhalli (2022) calculated the daily sentiment values of Singaporean netizens regarding the COVID-19 as a whole using related Facebook posts and used the Natural Language Processing (NLP) approach and constructed a multiple regression model.…”

Section: Online Emotions and Impactmentioning

confidence: 99%

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

Zhu¹,

Li²,

Li³

et al. 2023

OCS

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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 Bidirectional Encoder Representations from Transformers (BERT) contextual information through aspect-based sentiment analysis. In addition, this paper constructed a thesaurus of aspect words from the two stages of dynamic zero-COVID and orderly relaxation of epidemic control. This paper established the BERT-pair-ABSA model for semantic expansion of auxiliary sentences and calculated sentiment polarity to gain insight into the changes in netizens' concerns, emotional states and evolution trends at different stages. The research results showed: (1) Compared with the benchmark model, the proposed sentiment analysis model had better classification accuracy and was applicable to the sentiment classification of short texts in the epidemic situation; (2) During the dynamic zero-COVID stage, netizens paid attention to grassroots epidemic management and the scope of lockdown and epidemic control, which were closely related to both specific lockdown and control management, and the implementation of regional epidemic management; and (3) in the orderly relaxation stage of epidemic control, netizens were concerned about drug guarantee, medical care guarantee, personal health protection and health protection of special population groups, and negative emotions always dominated in drug guarantee, medical care guarantee and health protection of special groups. The negative sentiment of drug guarantee, medical care guarantee and health protection of special groups always dominated. The results provided an empirical basis for the optimization and adjustment of the anti-epidemic policies.

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“…Reddy et al [54] presented a Recurrent Neural Networks (RNN) methodology with LSTM that uses longitudinal healthcare sequential information, is promising in predicting lupus patients' readmission. Zhang et al [55] used Convolutional Neural Networks (CNN) to analyze Wuhan COVID-19 emergency data. Mou and Yu [56] introduced a CNN LSTM method of blood pressure prediction dependent on pulse wave information.…”

Section: Health Analytics Using Lstmmentioning

confidence: 99%

Context-Based, Predictive Access Control to Electronic Health Records

et al. 2022

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Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

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CNN-LSTM neural network model for fine-grained negative emotion computing in emergencies

Cited by 16 publications

References 38 publications

A Novel AB-CNN Model for Multi-Classification Sentiment Analysis of e-Commerce Comments

A Novel AB-CNN Model for Multi-Classification Sentiment Analysis of e-Commerce Comments

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

Context-Based, Predictive Access Control to Electronic Health Records

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