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Analysis of Public Sentiment regarding COVID-19 Vaccines on the Social Media Platform Reddit

Journal of Computer and Communications 2024年第2期12卷 80-108页

作者： Lucien Dikla Ngueleo Jules Pagna Disso armel ayimdji tekemetieu Justin Moskolaï Ngossaha Michael Nana Kameni African Institute for Mathematical Sciences (AIMS)/AIMS-Cameroon Limbe Cameroon Department of Mathematics and Computer Science Faculty of Science University of Douala Douala Cameroon WMG (Warwick Manufacturing Group) University of Warwick Coventry UK School of Information Studies McGill University Montreal Canada

This study undertakes a thorough analysis of the sentiment within the r/Corona-virus subreddit community regarding COVID-19 vaccines on Reddit. We meticulously collected and processed 34,768 comments, spanning from November 20, 2020, to January 17, 2021, using sentiment calculation methods such as TextBlob and Twitter-RoBERTa-Base-sentiment to categorize comments into positive, negative, or neutral sentiments. The methodology involved the use of Count Vectorizer as a vectorization technique and the implementation of advanced ensemble algorithms like XGBoost and Random Forest, achieving an accuracy of approximately 80%. Furthermore, through the Dirichlet latent allocation, we identified 23 distinct reasons for vaccine distrust among negative comments. These findings are crucial for understanding the community’s attitudes towards vaccination and can guide targeted public health messaging. Our study not only provides insights into public opinion during a critical health crisis, but also demonstrates the effectiveness of combining natural language processing tools and ensemble algorithms in sentiment analysis.

关键词： COVID-19 Vaccine TextBlob Twitter-RoBERTa-Base-Sentiment Sentiment Analysis Latent Dirichlet Allocation

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Towards A “Deep” Ontology for African Traditional Medicine

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Intelligent Information Management 2011年第6期3卷 244-251页

作者： armel ayimdji Souleymane Koussoubé Laure P. Fotso Balira O. Konfé 不详

The increasing interest on ontologies as the backbone technology for knowledge based systems implies the refinement of ontologies development methods. In this paper we propose a new approach to develop an ontology for African Traditional Medicine. The aim of our approach is to build a deep ontology by deepening concepts descriptions to formally represent all the semantics underlying the concepts used in African traditional medicine. We use a description logics language to formalize our approach.

关键词： Primary Ontology Deep Ontology Hidden Aspects Interpretations African Traditional Medicine Description Logics

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