Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets ; volume:13 ; number:1 ; day:9 ; month:4 ; year:2024 ; pages:1-21 ; date:12.2024
Journal of Cloud Computing ; 13, Heft 1 (9.4.2024), 1-21, 12.2024

Creator: Abdelhady, Naglaa
Hassan A. Soliman, Taysir
F. Farghally, Mohammed

Contributor: SpringerLink (Online service)

DOI: 10.1186/s13677-024-00644-6

URN: urn:nbn:de:101:1-2406241210078.938765241089

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:59 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Abdelhady, Naglaa
Hassan A. Soliman, Taysir
F. Farghally, Mohammed
SpringerLink (Online service)

Other Objects (12)

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Stacked model based argument extraction and stance detection using embedded LSTM model

Using spreadsheets to produce stacked histogram, stacked line and spindle charts

Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

Float-stacked graphene–PMMA laminate

Hydrogen in stacked dielectric layers

$3D printed stacked diffractive microlenses$

3D printed stacked diffractive microlenses

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Stacked model based argument extraction and stance detection using embedded LSTM model

Using spreadsheets to produce stacked histogram, stacked line and spindle charts

Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

Float-stacked graphene–PMMA laminate

Hydrogen in stacked dielectric layers

$3D printed stacked diffractive microlenses$

3D printed stacked diffractive microlenses

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Stacked model based argument extraction and stance detection using embedded LSTM model

Using spreadsheets to produce stacked histogram, stacked line and spindle charts

Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

Float-stacked graphene–PMMA laminate

Hydrogen in stacked dielectric layers

$3D printed stacked diffractive microlenses$

3D printed stacked diffractive microlenses

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Stacked model based argument extraction and stance detection using embedded LSTM model

Using spreadsheets to produce stacked histogram, stacked line and spindle charts

Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

Float-stacked graphene–PMMA laminate

Hydrogen in stacked dielectric layers

3D printed stacked diffractive microlenses

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Stacked model based argument extraction and stance detection using embedded LSTM model

Using spreadsheets to produce stacked histogram, stacked line and spindle charts

Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

Float-stacked graphene–PMMA laminate

Hydrogen in stacked dielectric layers

3D printed stacked diffractive microlenses

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Empowering Urdu sentiment analysis: an attention-based stacked CNN-Bi-LSTM DNN with multilingual BERT

Predicting Geolocation of Tweets: Using Combination of CNN and BiLSTM

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Stacked model based argument extraction and stance detection using embedded LSTM model

Using spreadsheets to produce stacked histogram, stacked line and spindle charts

Stacked ResNet-LSTM and CORAL model for multi-site air quality prediction

Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction

Float-stacked graphene–PMMA laminate

Hydrogen in stacked dielectric layers

3D printed stacked diffractive microlenses

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Application of bidirectional LSTM deep learning technique for sentiment analysis of COVID-19 tweets: post-COVID vaccination era

Related objects

Reset password