AI Article Synopsis
- Indonesia is gearing up for the 2024 elections, focusing on the Presidential, Regional Head, and Legislative positions, with the Presidential Election attracting significant attention on social media, especially Twitter.
- A sentiment analysis of Twitter users is being conducted to gauge opinions towards the main presidential candidates—Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan—using data collected via the Twitter API and Python.
- The analysis comprises 30,000 tweets collected in April 2023, structured and cleaned for accuracy, which can serve as a benchmark for future comparisons during the campaign period.
Article Abstract
Indonesia is one of the countries that is currently entering the political year for the election of President, Regional Heads, and Members of the Legislative in 2024. This has become a hot topic on social media, especially about the Presidential Election. Twitter is one of the platforms with the largest users in Indonesia. It is interesting to see the alignment of Twitter users towards presidential candidates who already have a carrying party, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan based on a sentiment analysis approach. User feedback data about Indonesian Presidential candidates are obtained from the Twitter platform using Twitter API with Python programming language. The data obtained was 30,000 data with each candidate as many as 10,000 data. Data is pulled in April 2023 with specific keywords. The time for data withdrawal is chosen based on the announcement of Presidential Candidates carried by political parties before the schedule for determining or campaigning for Presidential candidates. Current data can potentially be used again as a comparison of analysis of presidential candidates on campaign time spans and after campaigns or actual calculation results. The data that can be accessed is in CSV format and has gone through several stages such as labelling using Language experts, removing spam Tweets & empty cells and preprocessing.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10788203 | PMC |
| http://dx.doi.org/10.1016/j.dib.2023.109993 | DOI Listing |
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