Measuring the Degree of Divergence when Labeling Tweets in the Electoral Scenario | Zendy

Jéssica S. Santos | Zendy; Flávia Bernardini | Zendy; Aline Paes | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Measuring the Degree of Divergence when Labeling Tweets in the Electoral Scenario

Author(s) -

Jéssica S. Santos,

Flávia Bernardini,

Aline Paes

Publication year - 2021

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5753/brasnam.2021.16131

Subject(s) - divergence (linguistics) , annotation , computer science , social media , sentiment analysis , degree (music) , process (computing) , field (mathematics) , point (geometry) , presidential election , presidential system , kullback–leibler divergence , exploratory analysis , data science , information retrieval , artificial intelligence , politics , political science , world wide web , mathematics , linguistics , philosophy , physics , geometry , acoustics , pure mathematics , operating system , law

Analyzing electoral trends in political scenarios using social media with data mining techniques has become popular in recent years. A problem in this field is to reliably annotate data during the short period of electoral campaigns. In this paper, we present a methodology to measure labeling divergence and an exploratory analysis of data related to the 2018 Brazilian Presidential Elections. As a result, we point out some of the main characteristics that lead to a high level of divergence during the annotation process in this domain. Our analysis shows a high degree of divergence mainly in regard to sentiment labels. Also, a significant difference was identified between labels obtained by manual annotation and labels obtained using an automatic annotation approach.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research