Sentiment Analysis on Twitter Repercussion of Police Operations
ResumoViolence 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.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert:Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 1 (2019). https://doi.org/10.48550/arXiv.1810.04805
Laura C Hand and Brandon D Ching. 2020. Maintaining neutrality: A sentiment analysis of police agency Facebook pages before and after a fatal officer-involved shooting of a citizen. Government Information Quarterly 37, 1 (2020), 101420. https://doi.org/10.1016/j.giq.2019.101420
Tamanna Hossain, Robert L Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 Misinformation on Social Media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020. NLP COVID-19 Workshop, Event Online. https://doi.org/10.21275/ART20203995
Batta Mahesh. 2020. Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet] 9 (2020), 381–386. https://doi.org/10.21275/ART20203995
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. 2021. Deep learning–based text classification: a comprehensive review. ACM Computing Surveys (CSUR) 54, 3 (2021), 1–40. https://doi.org/10.1145/3439726
Saif M Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2017. Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT) 17, 3 (2017), 1–23. https://doi.org/10.1145/3003433
NEV-USP. 2022. Monitor da violência. https://nev.prp.usp.br/projetos/projetos-especiais/monitor-da-violencia agosto de 2022
Ashlin Oglesby-Neal, Emily Tiry, and K Kim. 2019. Public perceptions of police on social media. Washington, DC: Urban Institute 12 (2019).
Fábio Souza, Rodrigo Nogueira, and Roberto Lotufo. 2020. BERTimbau: pretrained BERT models for Brazilian Portuguese. In Brazilian Conference on Intelligent Systems. Springer International Publishing, Cham, 403–417. https://doi.org/10.1007/978-3-030-61377-8_28
Mike Thelwall. 2017. The Heart and soul of the web? Sentiment strength detection in the social web with SentiStrength. In Cyberemotions. Springer International Publishing, Cham, 119–134. https://doi.org/10.1007/978-3-319-43639-5_7