Sentiment Analysis of Social Media X Users on the Decline of Marriage Rates in Indonesia
DOI:
https://doi.org/10.25139/ijair.v7i1.9953Keywords:
Indonesian Marriage, Naïve Bayes, Sentiment Analysis, Support Vector Machine, Social Media XAbstract
This study aims to analyze public sentiment regarding Indonesia's declining marriage rates and identify the most accurate algorithm for sentiment analysis. Data were collected from the social media platform X using crawling techniques, resulting in 1,082 tweets that were processed and classified into positive, negative, and neutral sentiments. The findings reveal that most sentiments are positive at 41.31%, negative at 30.59%, and neutral at 28.10%. The classification model evaluation shows that SVM outperforms Naïve Bayes, achieving an accuracy of 74% compared to 53%. This study is limited to data collected from a single social media platform X. Future research is encouraged to expand the scope by collecting opinions from various social media platforms and exploring other machine learning or deep learning algorithms. The findings of this study are expected to contribute to policy-making efforts to improve marriage stability and well-being in Indonesia. This study also serves as a reference for academics and practitioners in understanding public opinion patterns on emerging social issues and providing a foundation for future studies on similar topics.
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