强制社会隔离:通过机器学习进行的情感分析

IF 0.3 Q4 BUSINESS
Carlos Alberto Arango Pastrana, Carlos Fernando Osorio Andrade
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引用次数: 3

摘要

为了降低Covid-19的传染率,哥伦比亚政府采取了强制隔离等措施,但意见不一,因为尽管有助于减少病毒的传播,但它会产生难以克服的精神和经济问题。本文件的目的是分析Twitter评论中与孤立相关的潜在情绪,确定在此背景下最常用的主题和词汇。建立了一种机器学习算法来识别72,564个帖子中的情绪,并应用社交网络分析来确定数据集中最常见的主题。结果表明,该算法在分类情感方面非常准确。此外,随着隔离的延长,与隔离有关的评论也成比例地增长。在哥伦比亚监禁期间,恐惧被认为是主要的感觉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aislamiento social obligatorio: un análisis de sentimientos mediante machine learning
To reduce the rate of contagion by Covid-19, the Colombian government has adopted, among other measures, for mandatory isolation, with divided opinions, because despite helping to reduce the spread of the virus, it generates mental and economic problems that are difficult to overcome. The objective of this document was to analyze the underlying sentiments in the Twitter comments related to isolation, identifying the topics and words most frequently used in this context. A machine learning algorithm was built to identify sentiments in 72,564 posts and a social network analysis was applied establishing the most frequent topics in the data sets. The results suggest that the algorithm is highly accurate in classifying feelings. Also, as the isolation extends, comments related to the quarantine grow proportionally. Fear was identified as the predominant feeling throughout the period of confinement in Colombia.
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来源期刊
Suma de Negocios
Suma de Negocios BUSINESS-
CiteScore
0.80
自引率
0.00%
发文量
5
审稿时长
8 weeks
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