A CNN-LSTM-Based Hybrid Deep Learning Approach for Sentiment Analysis on Monkeypox Tweets

Mohbey, Krishna Kumar; Meena, Gaurav; Kumar, Sunil; Lokesh, K.

doi:10.1007/s00354-023-00227-0

Cited by 29 publications

(6 citation statements)

References 28 publications

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“…Application domains where Explainable AI is used are also identified, along with the tools and platforms for its implementation. • One publication employed a CNN-LSTM-based hybrid architecture to analyze people's sentiment about Monkeypox disease on social media platforms [13]. An open-access dataset of tweets on Monkeypox documented in over 73 countries worldwide is used for this work.…”

Section: About This Special Issuementioning

confidence: 99%

The Way Forward with AI-Complete Problems

Groppe,

Jain

2024

New Gener. Comput.

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Section: About This Special Issuementioning

confidence: 99%

The Way Forward with AI-Complete Problems

Groppe,

Jain

2024

New Gener. Comput.

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“…A hybrid deep learning strategy based on Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) was presented by Mohbey et al [22] for sentiment analysis of monkeypox tweets. The main objective was to find out how the public feels about the then current monkeypox outbreak so as to help policymakers have a better understanding of how the public perceives the disease.…”

Section: Literature Surveymentioning

confidence: 99%

Weighted Ensemble LSTM Model with Word Embedding Attention for E-Commerce Product Recommendation

Sharma,

Sagvekar

2023

JCOMSS

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Nowadays, the proliferation of social media and ecommerce platforms is largely due to the development of internet technology. Additionally, consumers are used to the idea of using these platforms to share their thoughts and feelings with others through text or multimedia data. However, it is difficult to identify the best categorization methods for this type of data. Furthermore, users are seen to have difficulty understanding aspect-based feelings conveyed by other users, and the currently existing models' accuracies are not up to par. Deep learning models used for sentiment analysis (SA) provide improved performance by finding out the actual emotions in the presented data. The aim of this research is to develop a weighted ensemble with Long Short-Term Memory (LSTM), and a specialised deep learning model using unique word embedding approaches to enhance its performance in sentiment analysis. The words with a strong connection to a particular class are given more weight by the Word Embedding Attention (WEA) technique. The weighted ensemble with LSTM yields superior outcomes because of its excellent generalization capabilities. By integrating the advantages of several models and mitigating the effects of each model's shortcomings, ensemble voting raises the prediction accuracy. By lessening the influence of outliers or errors in individual model categorization, ensemble voting increases the robustness of categorization.This LSTM weighted ensemble achieves 99.82 % accuracy, 99.4% precision, 99.02% f-score, and 99.7% recall in sentiment analysis which is much higher when compared to the outcomes of conventional methods.

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“…Mohbey et al. ( 21 ) presenting a hybrid technique based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). In this study a knowledge graph of related events based on Twitter data, which provides a real-time and eventful source of new information.…”

Section: Introductionmentioning

confidence: 99%

A study on the recognition of monkeypox infection based on deep convolutional neural networks

Chen,

Han

2023

Front. Immunol.

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IntroductionThe World Health Organization (WHO) has assessed the global public risk of monkeypox as moderate, and 71 WHO member countries have reported more than 14,000 cases of monkeypox infection. At present, the identification of clinical symptoms of monkeypox mainly depends on traditional medical means, which has the problems of low detection efficiency and high detection cost. The deep learning algorithm is excellent in image recognition and can extract and recognize image features quickly and reliably.MethodsTherefore, this paper proposes a residual convolutional neural network based on the λ function and contextual transformer (LaCTResNet) for the image recognition of monkeypox cases.ResultsThe average recognition accuracy of the neural network model is 91.85%, which is 15.82% higher than that of the baseline model ResNet50 and better than the classical convolutional neural networks models such as AlexNet, VGG16, Inception-V3, and EfficientNet-B5.DiscussionThis method realizes high-precision identification of skin symptoms of the monkeypox virus to provide a fast and reliable auxiliary diagnosis method for monkeypox cases for front-line medical staff.

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A CNN-LSTM-Based Hybrid Deep Learning Approach for Sentiment Analysis on Monkeypox Tweets

Cited by 29 publications

References 28 publications

The Way Forward with AI-Complete Problems

The Way Forward with AI-Complete Problems

Weighted Ensemble LSTM Model with Word Embedding Attention for E-Commerce Product Recommendation

A study on the recognition of monkeypox infection based on deep convolutional neural networks

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