Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models

Khandaker Tayef Shahriar; Md Musfique Anwar; Iqbal H. Sarker

doi:10.1007/978-3-031-34619-4_31

Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models

Khandaker Tayef Shahriar^*
, Md Musfique Anwar
, Iqbal H. Sarker

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper takes into account the aspect-based sentiment analysis of COVID-19 tweets, in order to understand human emotions and provide decision support to policymakers. People these days use social media to share thoughts and feelings in critical situations like COVID-19. After the World Health Organization (WHO) declared COVID-19 a pandemic, a significant increase in the usage of the most influential Twitter platforms has been observed. Thus, it is impossible to manually track all the COVID-19-related tweets on the Twitter platform. Sentiment analysis is one of the solutions to this problem. In this work, we attempt to understand people’s feelings about certain aspects by analyzing the COVID-19 tweets to reduce the harmful consequences of the pandemic and to understand the crisis, humanitarian needs and measures. We, therefore, propose a framework for the aspect based sentiment analysis of COVID-19 tweets by extracting the top ten aspects and classifying positive, negative, or neutral tweets in each aspect using the blending ensemble of basic deep learning models. The experimental results show that the proposed framework achieves the highest accuracy of 85.65% compared to other benchmark deep learning models.

Original language	English
Title of host publication	Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings
Editors	Md. Shahriare Satu, Mohammad Ali Moni, M. Shamim Kaiser, Mohammad Shamsul Arefin, Mohammad Shamsul Arefin
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	386-400
Number of pages	15
ISBN (Print)	9783031346187
DOIs	https://doi.org/10.1007/978-3-031-34619-4_31
State	Published - 2023
Externally published	Yes
Event	1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022 - Noakhali, Bangladesh Duration: 23 Sep 2022 → 25 Sep 2022

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	490 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022
Country/Territory	Bangladesh
City	Noakhali
Period	23/09/22 → 25/09/22

Bibliographical note

Publisher Copyright:
© 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Aspect based
Blending ensemble
COVID-19
Deep learning
LDA
Sentiment analysis
Tweets

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1007/978-3-031-34619-4_31

Cite this

Shahriar, K. T., Anwar, M. M., & Sarker, I. H. (2023). Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models. In M. S. Satu, M. A. Moni, M. S. Kaiser, M. S. Arefin, & M. S. Arefin (Eds.), Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings (pp. 386-400). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 490 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34619-4_31

Shahriar, Khandaker Tayef ; Anwar, Md Musfique ; Sarker, Iqbal H. / Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models. Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings. editor / Md. Shahriare Satu ; Mohammad Ali Moni ; M. Shamim Kaiser ; Mohammad Shamsul Arefin ; Mohammad Shamsul Arefin. Springer Science and Business Media Deutschland GmbH, 2023. pp. 386-400 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{b81e68eea7314c31a6c2650f1a04804e,

title = "Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models",

abstract = "This paper takes into account the aspect-based sentiment analysis of COVID-19 tweets, in order to understand human emotions and provide decision support to policymakers. People these days use social media to share thoughts and feelings in critical situations like COVID-19. After the World Health Organization (WHO) declared COVID-19 a pandemic, a significant increase in the usage of the most influential Twitter platforms has been observed. Thus, it is impossible to manually track all the COVID-19-related tweets on the Twitter platform. Sentiment analysis is one of the solutions to this problem. In this work, we attempt to understand people{\textquoteright}s feelings about certain aspects by analyzing the COVID-19 tweets to reduce the harmful consequences of the pandemic and to understand the crisis, humanitarian needs and measures. We, therefore, propose a framework for the aspect based sentiment analysis of COVID-19 tweets by extracting the top ten aspects and classifying positive, negative, or neutral tweets in each aspect using the blending ensemble of basic deep learning models. The experimental results show that the proposed framework achieves the highest accuracy of 85.65\% compared to other benchmark deep learning models.",

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Shahriar, KT, Anwar, MM & Sarker, IH 2023, Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models. in MS Satu, MA Moni, MS Kaiser, MS Arefin & MS Arefin (eds), Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 490 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 386-400, 1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, Noakhali, Bangladesh, 23/09/22. https://doi.org/10.1007/978-3-031-34619-4_31

Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models. / Shahriar, Khandaker Tayef; Anwar, Md Musfique; Sarker, Iqbal H.
Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings. ed. / Md. Shahriare Satu; Mohammad Ali Moni; M. Shamim Kaiser; Mohammad Shamsul Arefin; Mohammad Shamsul Arefin. Springer Science and Business Media Deutschland GmbH, 2023. p. 386-400 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 490 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper takes into account the aspect-based sentiment analysis of COVID-19 tweets, in order to understand human emotions and provide decision support to policymakers. People these days use social media to share thoughts and feelings in critical situations like COVID-19. After the World Health Organization (WHO) declared COVID-19 a pandemic, a significant increase in the usage of the most influential Twitter platforms has been observed. Thus, it is impossible to manually track all the COVID-19-related tweets on the Twitter platform. Sentiment analysis is one of the solutions to this problem. In this work, we attempt to understand people’s feelings about certain aspects by analyzing the COVID-19 tweets to reduce the harmful consequences of the pandemic and to understand the crisis, humanitarian needs and measures. We, therefore, propose a framework for the aspect based sentiment analysis of COVID-19 tweets by extracting the top ten aspects and classifying positive, negative, or neutral tweets in each aspect using the blending ensemble of basic deep learning models. The experimental results show that the proposed framework achieves the highest accuracy of 85.65% compared to other benchmark deep learning models.

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KW - Blending ensemble

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KW - Deep learning

KW - LDA

KW - Sentiment analysis

KW - Tweets

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Shahriar KT, Anwar MM, Sarker IH. Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models. In Satu MS, Moni MA, Kaiser MS, Arefin MS, Arefin MS, editors, Machine Intelligence and Emerging Technologies - First International Conference, MIET 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 386-400. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-031-34619-4_31