@inproceedings{amine-romdhane-etal-2021-sifting,
title = "Sifting {F}rench Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety",
author = "Amine Romdhane, Mohamed and
Cabrio, Elena and
Villata, Serena",
editor = "Denis, Pascal and
Grabar, Natalia and
Fraisse, Amel and
Cardon, R{\'e}mi and
Jacquemin, Bernard and
Kergosien, Eric and
Balvet, Antonio",
booktitle = "Actes de la 28e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conf{\'e}rence principale",
month = "6",
year = "2021",
address = "Lille, France",
publisher = "ATALA",
url = "https://aclanthology.org/2021.jeptalnrecital-taln.21",
pages = "219--226",
abstract = "Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety. Social media can be leveraged to understand public sentiment and feelings in real-time, and target public health messages based on user interests and emotions. In this paper, we investigate the impact of the COVID-19 pandemic in triggering intense anxiety, relying on messages exchanged on Twitter. More specifically, we provide : i) a quantitative and qualitative analysis of a corpus of tweets in French related to coronavirus, and ii) a pipeline approach (a filtering mechanism followed by Neural Network methods) to satisfactory classify messages expressing intense anxiety on social media, considering the role played by emotions.",
}
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%0 Conference Proceedings
%T Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety
%A Amine Romdhane, Mohamed
%A Cabrio, Elena
%A Villata, Serena
%Y Denis, Pascal
%Y Grabar, Natalia
%Y Fraisse, Amel
%Y Cardon, Rémi
%Y Jacquemin, Bernard
%Y Kergosien, Eric
%Y Balvet, Antonio
%S Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale
%D 2021
%8 June
%I ATALA
%C Lille, France
%F amine-romdhane-etal-2021-sifting
%X Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety. Social media can be leveraged to understand public sentiment and feelings in real-time, and target public health messages based on user interests and emotions. In this paper, we investigate the impact of the COVID-19 pandemic in triggering intense anxiety, relying on messages exchanged on Twitter. More specifically, we provide : i) a quantitative and qualitative analysis of a corpus of tweets in French related to coronavirus, and ii) a pipeline approach (a filtering mechanism followed by Neural Network methods) to satisfactory classify messages expressing intense anxiety on social media, considering the role played by emotions.
%U https://aclanthology.org/2021.jeptalnrecital-taln.21
%P 219-226
Markdown (Informal)
[Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety](https://aclanthology.org/2021.jeptalnrecital-taln.21) (Amine Romdhane et al., JEP/TALN/RECITAL 2021)
ACL