Delineation of The Early 2024 Election Map: Sentiment Analysis Approach to Twitter Data

Authors

  • Nur Ulum Rahmanulloh Department of Statistical Computing, Politeknik Statistika STIS, Indonesia
  • Ibnu Santoso Department of Statistical Computing, Politeknik Statistika STIS, Indonesia

DOI:

https://doi.org/10.15575/join.v7i2.925

Keywords:

Politics, 2024 Election, Sentiment Analysis, Twitter

Abstract

As a democratic country, the people hold an important role in determining power in Indonesia. The closest political agenda in Indonesia is the 2024 Election. A survey has been conducted by several private survey agencies regarding the 2024 political map which has revealed the top five names, namely Prabowo Subianto, Ganjar Pranowo, Anies Baswedan, Sandiaga Uno, and Ridwan Kamil. This study aims to describe the initial map of the 2024 Election through a sentiment analysis approach to Twitter data. This study uses tweet data that mentions five political figures during 2021. In general, the demographic condition of Twitter users that pros or cons to five political figures, among them: located on the Java, in the age group 19–29 years old, and male.  The sentiment analysis method used is supervised learning with different methods for each figure. The difference in methods adjusts the best evaluation value given in each figure. The results showed that the highest positive sentimental tweets and the highest number of pro accounts was about Ganjar Pranowo. On the other hand, the highest negative sentiment and the highest number of contra accounts was about Prabowo Subianto. Many words that often appear on a figure's positive sentiment are expressions of hope, prayer, and support. On negative tweets, the word that comes up a lot relating to the work field or work region of the figures. 

Author Biographies

Nur Ulum Rahmanulloh, Department of Statistical Computing, Politeknik Statistika STIS

Department of Statistical Computing

Ibnu Santoso, Department of Statistical Computing, Politeknik Statistika STIS

Department of Statistical Computing

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Published

2022-12-29

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