@inproceedings{wehrli-etal-2024-guiding,
title = "Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the {G}erman {X}/{T}witter Discourse on Face Masks in the 2020 {COVID}-19 Pandemic",
author = "Wehrli, Silvan and
Ezekannagha, Chisom and
Hattab, Georges and
Boender, Tamara and
Arnrich, Bert and
Irrgang, Christopher",
editor = "De Clercq, Orph{\'e}e and
Barriere, Valentin and
Barnes, Jeremy and
Klinger, Roman and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam",
booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wassa-1.13",
doi = "10.18653/v1/2024.wassa-1.13",
pages = "153--167",
abstract = "Social media are a critical component of the information ecosystem during public health crises. Understanding the public discourse is essential for effective communication and misinformation mitigation. Computational methods can aid these efforts through online social listening. We combined hierarchical text clustering and sentiment analysis to examine the face mask-wearing discourse in Germany during the COVID-19 pandemic using a dataset of 353,420 German X (formerly Twitter) posts from 2020. For sentiment analysis, we annotated a subsample of the data to train a neural network for classifying the sentiments of posts (neutral, negative, or positive). In combination with clustering, this approach uncovered sentiment patterns of different topics and their subtopics, reflecting the online public response to mask mandates in Germany. We show that our approach can be used to examine long-term narratives and sentiment dynamics and to identify specific topics that explain peaks of interest in the social media discourse.",
}
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%0 Conference Proceedings
%T Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 Pandemic
%A Wehrli, Silvan
%A Ezekannagha, Chisom
%A Hattab, Georges
%A Boender, Tamara
%A Arnrich, Bert
%A Irrgang, Christopher
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wehrli-etal-2024-guiding
%X Social media are a critical component of the information ecosystem during public health crises. Understanding the public discourse is essential for effective communication and misinformation mitigation. Computational methods can aid these efforts through online social listening. We combined hierarchical text clustering and sentiment analysis to examine the face mask-wearing discourse in Germany during the COVID-19 pandemic using a dataset of 353,420 German X (formerly Twitter) posts from 2020. For sentiment analysis, we annotated a subsample of the data to train a neural network for classifying the sentiments of posts (neutral, negative, or positive). In combination with clustering, this approach uncovered sentiment patterns of different topics and their subtopics, reflecting the online public response to mask mandates in Germany. We show that our approach can be used to examine long-term narratives and sentiment dynamics and to identify specific topics that explain peaks of interest in the social media discourse.
%R 10.18653/v1/2024.wassa-1.13
%U https://aclanthology.org/2024.wassa-1.13
%U https://doi.org/10.18653/v1/2024.wassa-1.13
%P 153-167
Markdown (Informal)
[Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 Pandemic](https://aclanthology.org/2024.wassa-1.13) (Wehrli et al., WASSA-WS 2024)
ACL
- Silvan Wehrli, Chisom Ezekannagha, Georges Hattab, Tamara Boender, Bert Arnrich, and Christopher Irrgang. 2024. Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 Pandemic. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 153–167, Bangkok, Thailand. Association for Computational Linguistics.