Analyzing public sentiment towards the Covid-19 pandemic: a Twitter-based sentiment analysis and machine learning approach

Andreas Kanavos; Nikos Antonopoulos; Alaa Mohasseb; Phivos Mylonas

doi:10.1109/SMAP59435.2023.10255176

Analyzing public sentiment towards the Covid-19 pandemic: a Twitter-based sentiment analysis and machine learning approach

Andreas Kanavos^*
, Nikos Antonopoulos
, Alaa Mohasseb
, Phivos Mylonas

^*Corresponding author for this work

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

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Abstract

The Covid-19 pandemic has significantly reshaped societies, prompting an unprecedented surge in digital interactions and communication via social media platforms. Amidst this evolving landscape, the analysis of public sentiment towards pandemic management strategies has emerged as a critical avenue for understanding societal responses. This paper delves into the intricate landscape of sentiment dynamics surrounding the Covid-19 pandemic, with a focus on sentiments expressed in Twitter posts. Leveraging sentiment analysis techniques, the study provides nuanced insights into societal attitudes, concerns, and perceptions across distinct phases of the pandemic. The investigation not only employs traditional lexicon-based sentiment analysis but also explores the intersection of sentiment analysis and machine learning algorithms. The research contributes to the discourse by presenting a comprehensive analysis of public sentiment dynamics during various pandemic stages, shedding light on evolving emotional responses and offering insights into the effectiveness of measures.

Original language	English
Title of host publication	18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9798350327717
ISBN (Print)	9798350327724
DOIs	https://doi.org/10.1109/SMAP59435.2023.10255176
Publication status	Published - 26 Sept 2023
Event	The 18th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2023) - Limassol, Cyprus Duration: 25 Sept 2023 → 26 Sept 2023 https://cyprusconferences.org/smap2023/

Conference

Conference	The 18th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2023)
Country/Territory	Cyprus
City	Limassol
Period	25/09/23 → 26/09/23
Internet address	https://cyprusconferences.org/smap2023/

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1109/SMAP59435.2023.10255176

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Accepted author manuscript (Post-print), 276 KB

https://cyprusconferences.org/smap2023/

Cite this

Kanavos, A., Antonopoulos, N., Mohasseb, A., & Mylonas, P. (2023). Analyzing public sentiment towards the Covid-19 pandemic: a Twitter-based sentiment analysis and machine learning approach. In 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP) Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMAP59435.2023.10255176

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Kanavos, A, Antonopoulos, N, Mohasseb, A & Mylonas, P 2023, Analyzing public sentiment towards the Covid-19 pandemic: a Twitter-based sentiment analysis and machine learning approach. in 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). Institute of Electrical and Electronics Engineers Inc., The 18th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP 2023), Limassol, Cyprus, 25/09/23. https://doi.org/10.1109/SMAP59435.2023.10255176

Analyzing public sentiment towards the Covid-19 pandemic: a Twitter-based sentiment analysis and machine learning approach. / Kanavos, Andreas; Antonopoulos, Nikos; Mohasseb, Alaa et al.
18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). Institute of Electrical and Electronics Engineers Inc., 2023.

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

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Analyzing public sentiment towards the Covid-19 pandemic: a Twitter-based sentiment analysis and machine learning approach

Abstract

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UN SDGs

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