Predicting political mood tendencies based on Twitter data

Hernandez-Suarez, Aldo; Sánchez-Pérez, Gabriel; Martínez-Hernández, V.; Pérez-Meana, Héctor; Toscano-Medina, Karina; Nakano-Miyatake, Mariko; Sánchez, Vı́ctor

doi:10.1109/iwbf.2017.7935106

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…The evaluation results in a Mean Absolute Error (MAE) of 0.63%. Suarez et al [151] show the co-relation between Twitter sentiments and political preferences in a certain period. The findings however are not generalizable for overall prediction.…”

Section: ) Political Campaignsmentioning

confidence: 99%

A Survey on Computational Politics

et al. 2020

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Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users' participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

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Section: ) Political Campaignsmentioning

confidence: 99%

A Survey on Computational Politics

et al. 2020

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“…Nofer, et al [36] predicted stock returns by extracting user sentiment levels from Twitter. Tumasjan, et al [37] predicted sentiment trends in political events (government, elections, security and defense, and health insurance) by processing and classifying the data in Twitter. Zhao, et al [38] proposed a deep event-emotion analysis system on Microblogs, explored the causes of different emotions in heated events.…”

Section: Emotional Analysismentioning

confidence: 99%

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

Gao

et al. 2020

Sustainability

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(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

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“…Tomando como estudio de caso las elecciones presidenciales de Estados Unidos en 2016, tres estudios independientes [19]- [21] muestran una caracterización completa de los usuarios de redes sociales como Twitter y sus preferencias políticas.…”

Section: Análisis De Sentimientos Y Otras Técnicas De Predicciónunclassified

Predicción electoral usando un modelo híbrido basado en análisis sentimental y seguimiento a encuestas: elecciones presidenciales de Colombia

Callejas-Cuervo

Vélez-Guerrero

2019

Rev. politec.

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La disponibilidad de los medios digitales ha proporcionado una poderosa herramienta para expresar opiniones incluyendo aspectos sociales y políticos desarrollados en cada región. En Colombia, el uso de redes sociales ha dado lugar a la difusión masiva de opiniones políticas, especialmente durante el período de campaña en las elecciones presidenciales nacionales. Este trabajo propone un modelo híbrido para predecir el desenlace de la primera vuelta en las elecciones presidenciales de Colombia en 2018 (pre-hoc), cuyo objetivo es minimizar el error absoluto y mejorar la calidad de la predicción final. Las actividades de los usuarios en Twitter y Facebook fueron registradas y analizadas, obteniendo como resultado una predicción precisa y coherente con la realidad, donde el RMSE del modelo híbrido ronda el 2,47%, superando en promedio el RMSE de las firmas encuestadoras tradicionales más prominentes del país. Adicionalmente también se predijo el valor del abstencionismo electoral con un error diferencial de 1,72% con respecto al valor real, demostrando la confiabilidad de la metodología propuesta.

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Predicting political mood tendencies based on Twitter data

Cited by 13 publications

References 16 publications

A Survey on Computational Politics

A Survey on Computational Politics

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

Predicción electoral usando un modelo híbrido basado en análisis sentimental y seguimiento a encuestas: elecciones presidenciales de Colombia

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