A Study of Public Opinion on the 2024 Regional Elections Using Cosine Similarity and TF-IDF Algorithms

The organization of general and regional head elections is an essential aspect of implementing an indirect democracy system. The primary objective of regional elections is to ensure that leaders are elected democratically and act on behalf of the people. The simultaneous holding of regional head elections has become a major topic of public discussion, giving rise to diverse opinions, particularly among Twitter users. This study aims to classify public opinion regarding the 2024 regional head elections using TF-IDF weighting, followed by a classification process with the Cosine Similarity algorithm. Of the 1,000 data points successfully scraped, 34.9% were classified as positive sentiment, 23.5% as negative sentiment, and 37.1% as neutral sentiment

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Publication Details

Journal
Operations Research International Conference Series
Published
2025-04-23
DOI
https://doi.org/10.47194/orics.v6i1.364
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
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article

A Study of Public Opinion on the 2024 Regional Elections Using Cosine Similarity and TF-IDF Algorithms

Arie Qur’ania, Mahboob Iqbal, A. Hidayat
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Operations Research International Conference Series
Sentiment Analysis and Opinion Mining
article

A Study of Public Opinion on the 2024 Regional Elections Using Cosine Similarity and TF-IDF Algorithms

Arie Qur’ania, Mahboob Iqbal, A. Hidayat
article en

Abstract

The organization of general and regional head elections is an essential aspect of implementing an indirect democracy system. The primary objective of regional elections is to ensure that leaders are elected democratically and act on behalf of the people. The simultaneous holding of regional head elections has become a major topic of public discussion, giving rise to diverse opinions, particularly among Twitter users. This study aims to classify public opinion regarding the 2024 regional head elections using TF-IDF weighting, followed by a classification process with the Cosine Similarity algorithm. Of the 1,000 data points successfully scraped, 34.9% were classified as positive sentiment, 23.5% as negative sentiment, and 37.1% as neutral sentiment

Operations Research International Conference SeriesVol. 6(1)
Openalex Percentile: Top 72%
Sentiment Analysis and Opinion Mining
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