Sentiment Analysis on Twitter Repercussion of Police Operations

Marcos Fontes Feitosa, Saul Rocha, G. Gonçalves, C. H. G. Ferreira, Jussara M. Almeida
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引用次数: 1

Abstract

Violence and a sense of insecurity are among the main problems in urban centres. In Brazil, an average rate of 20 deaths per month is estimated for every 100,000 inhabitants due to violence. Virtual social networks are increasingly used as a means for users to express their opinions or indignation about this problem. In this article, we analyze the sentiment of users in comments shared on Twitter about police operations with great repercussions in news portals in Brazil. In this sense, we explore lexicon and machine learning models to understand the emotion in which users discuss public safety on social networks and their opinion about the work of government agencies to reduce violence in cities. Our experiments show how challenging this inference is given peculiar characteristics of the context, such as mostly negative and sarcastic expressions. Nevertheless, our best classifiers achieved accuracy and specificity (macro F1) greater than 60% for identifying sentiments polarity, indicating a promising methodology for automatically inferring public opinion about police operations.
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警察行动推特反响的情绪分析
暴力和不安全感是城市中心的主要问题。在巴西,估计每月每10万居民中平均有20人死于暴力。虚拟社交网络越来越多地被用户用来表达他们对这个问题的意见或愤慨。在这篇文章中,我们分析了用户在Twitter上分享的评论中对警方行动的看法,这些行动在巴西的新闻门户网站上产生了巨大的影响。从这个意义上说,我们探索了词汇和机器学习模型,以了解用户在社交网络上讨论公共安全时的情绪,以及他们对政府机构减少城市暴力工作的看法。我们的实验表明,在语境的特殊特征下,比如大多数是消极和讽刺的表达,这种推断是多么具有挑战性。尽管如此,我们最好的分类器在识别情绪极性方面实现了超过60%的准确性和特异性(宏观F1),这表明一种有前途的方法可以自动推断公众对警察行动的看法。
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