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Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
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In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
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US-amerikanische Jiddische und Pennsylvania-Deutsche Medien zwischen lokaler Verankerung und Transnationalisierung
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In: ISSN: 0014-2115 ; EISSN: 2426-5543 ; Etudes Germaniques ; https://halshs.archives-ouvertes.fr/halshs-03559078 ; Etudes Germaniques, Klincksieck, 2022, Les études germaniques et le transnational : enjeux d’un questionnement scientifique et épistémologique, 76 (3), pp.379-398 (2022)
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| 3 |
« “Twitta” “Intellectuelle” “Influenceuse” ? Être enseignante-chercheuse sur twitter »
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In: ISSN: 1763-0061 ; EISSN: 1963-1812 ; Tracés : Revue de Sciences Humaines ; https://hal.archives-ouvertes.fr/hal-03592945 ; Tracés : Revue de Sciences Humaines, ENS Éditions, A paraître (2022)
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| 4 |
Renouvellement paradigmatique dans l’analyse des discours numériques : le cas de la communication politique sur les RSN
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In: ISSN: 2116-1747 ; Etudes de stylistique anglaise ; https://hal-amu.archives-ouvertes.fr/hal-03584927 ; Etudes de stylistique anglaise, Société de stylistique anglaise, Lyon, 2022, Renaissance(s)/Rebirth(s), ⟨10.4000/esa.4816⟩ ; https://journals.openedition.org/esa/4816 (2022)
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Chapter 11. Consumer opinion about smoked bacon using Twitter and textual analysis: The challenge continues
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In: Sensory Analysis for the Development of Meat Products ; https://hal-agrosup-dijon.archives-ouvertes.fr/hal-03575175 ; Sensory Analysis for the Development of Meat Products, Elsevier, pp.181-196, 2022, 9780128228326. ⟨10.1016/B978-0-12-822832-6.00013-8⟩ (2022)
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| 6 |
Förderung des Bildungsspracherwerbs bei heterogenen sprachlichen Voraussetzungen im Unterricht mit digitalen Medien ...
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#Bittersweet: Positive, negative, and mixed emotions in twitter posts ...
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| 8 |
Zum Ungleichgewicht digital vermittelten Sachunterrichts und sprachlich-kommunikativer Anforderungen ...
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| 10 |
Sprachliche Individualisierung mittels digitaler Medien
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In: Haider, Michael [Hrsg.]; Schmeinck, Daniela [Hrsg.]: Digitalisierung in der Grundschule. Grundlagen, Gelingensbedingungen und didaktische Konzeptionen am Beispiel des Fachs Sachunterricht. Bad Heilbrunn : Verlag Julius Klinkhardt 2022, S. 140-153 (2022)
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| 11 |
Förderung des Bildungsspracherwerbs bei heterogenen sprachlichen Voraussetzungen im Unterricht mit digitalen Medien
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In: Haider, Michael [Hrsg.]; Schmeinck, Daniela [Hrsg.]: Digitalisierung in der Grundschule. Grundlagen, Gelingensbedingungen und didaktische Konzeptionen am Beispiel des Fachs Sachunterricht. Bad Heilbrunn : Verlag Julius Klinkhardt 2022, S. 124-139 (2022)
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| 12 |
Zum Ungleichgewicht digital vermittelten Sachunterrichts und sprachlich-kommunikativer Anforderungen
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In: Sachunterricht in der Informationsgesellschaft. Bad Heilbrunn : Verlag Julius Klinkhardt 2022, S. 114-121. - (Probleme und Perspektiven des Sachunterrichts; 32) (2022)
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| 13 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
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| 14 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
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| 17 |
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
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| 18 |
Extracting Disaster-Related Location Information through Social Media to Assist Remote Sensing for Disaster Analysis: The Case of the Flood Disaster in the Yangtze River Basin in China in 2020
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In: Remote Sensing; Volume 14; Issue 5; Pages: 1199 (2022)
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| 19 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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Abstract:
The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora.
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Keyword:
adverse drug events; annotated corpus; coreference relation extraction; deep learning; information extraction; language models; machine learning; MESHRUS; named entity recognition; neural networks; pharmacovigilance; social media; UMLS
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URL: https://doi.org/10.3390/app12010491
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| 20 |
Social Media and the Pandemic: Consumption Habits of the Spanish Population before and during the COVID-19 Lockdown
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In: Sustainability; Volume 14; Issue 9; Pages: 5490 (2022)
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