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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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« “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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| 15 |
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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| 16 |
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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| 17 |
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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| 18 |
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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| 19 |
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
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In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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Abstract:
The emissions of greenhouse gases, such as carbon dioxide, into the biosphere have the consequence of warming up the planet, hence the existence of climate change. Sentiment analysis has been a popular subject and there has been a plethora of research conducted in this area in recent decades, typically on social media platforms such as Twitter, due to the proliferation of data generated today during discussions on climate change. However, there is not much research on the performances of different sentiment analysis approaches using lexicon, machine learning and hybrid methods, particularly within this domain-specific sentiment. This study aims to find the most effective sentiment analysis approach for climate change tweets and related domains by performing a comparative evaluation of various sentiment analysis approaches. In this context, seven lexicon-based approaches were used, namely SentiWordNet, TextBlob, VADER, SentiStrength, Hu and Liu, MPQA, and WKWSCI. Meanwhile, three machine learning classifiers were used, namely Support Vector Machine, Naïve Bayes, and Logistic Regression, by using two feature extraction techniques, which were Bag-of-Words and TF–IDF. Next, the hybridization between lexicon-based and machine learning-based approaches was performed. The results indicate that the hybrid method outperformed the other two approaches, with hybrid TextBlob and Logistic Regression achieving an F1-score of 75.3%; thus, this has been chosen as the most effective approach. This study also found that lemmatization improved the accuracy of machine learning and hybrid approaches by 1.6%. Meanwhile, the TF–IDF feature extraction technique was slightly better than BoW by increasing the accuracy of the Logistic Regression classifier by 0.6%. However, TF–IDF and BoW had an identical effect on SVM and NB. Future works will include investigating the suitability of deep learning approaches toward this domain-specific sentiment on social media platforms.
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Keyword:
climate change; lexicon; machine learning; sentiment analysis; social media
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URL: https://doi.org/10.3390/su14084723
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| 20 |
Artificial Intelligent in Education
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In: Sustainability; Volume 14; Issue 5; Pages: 2862 (2022)
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