2021, Number 5
Conversations on Cuban medical delegations in the context of COVID-19 from the info-communicative environment on Twitter
Language: Spanish
References: 20
Page: 1-8
PDF size: 713.31 Kb.
ABSTRACT
Introduction: The COVID-19 pandemic modifies the info-communicative environment of cyber users. Cuba, in particular, presents a remarkable level of technological development inserted in the Internet social platform, Twitter. This support plays a key role in the debate about issues related to the Cuban medical delegations due to their confrontation with the virus at the international level, which contributes to the formation of a favorable global opinion. The aforementioned point is expressed from the online conversations and their speeches in this site.Objective: To characterize the info-communicative environment on Twitter during the online conversations about Cuban medical brigades between December 31, 2020 and January 18, 2021.
Material and Methods: The hermeneutic theoretical method is used for the description of the results of the statistical processing of content, structure and use on the Web, corresponding to a transdisciplinary approach through the application of Data Mining Technique as an instrument of the unsupervised algorithm SSA-UO for sentiment analysis on Twitter.
Results: The characteristics of the info-communicative environment on Twitter during the online conversations about Cuban medical delegations evidence a predominance of positive polarity.
Conclusions: Retweets, mentions and the publication of links influence the particularities of the debate on the topic in the microblogging sphere.
REFERENCES
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Ortega R, Fonseca A, Gutiérrez Y, Montoyo A. SSA-UO: Unsupervised Twitter Sentiment Analysis. Second Joint Conference on Lexical and Computational Semantics of the Seventh International Workshop on Semantic Evaluation; 2013 june [Internet]. Georgia: Association for Computational Linguistics; 2013 .p.501-7 [Citado 30/01/2021] Disponible en: Disponible en: https://www.academia.edu/7553479/SSA_UO_Unsupervised_Twitter_Sentiment_Analysis