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All Journal Journal of Islamic Economics and Business Ethics
Bayu Tri Cahya
Universitas Islam Negeri Sunan Kudus, Indonesia
Arts Economics, Econometrics & Finance

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Machine Learning-Based Sentiment Analysis as a Dynamic Early Warning Sign for Financial Distress in Property and Real Estate Companies Revina Sofia Septiani; Bayu Tri Cahya; Nor ishah Mohd Ali
JOURNAL OF ISLAMIC ECONOMICS AND BUSSINES ETHICS Vol 3 No 2 (2026): JIESBI: Journal of Islamic Economics and Business Ethics
Publisher : IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/jiesbi.v3i2.593

Abstract

This study investigates the integration of sentiment analysis from social media and digital news portals as predictors of financial distress in Indonesian property companies. Intensifying industrial competition in 2024, as reported by KPPU data, has led to shrinking market shares and declining profits. Companies failing to compete face heightened risks of financial distress, necessitating more proactive monitoring tools. Unlike traditional models relying solely on financial ratios, this research innovates by using investor sentiment as an early warning sign, utilizing advanced crawling techniques like Tweet Harvest and high-performance analysis through RapidMiner. Research methods a total of 1,965 tweets and 602 news articles (2020–2023) were extracted and classified using Naïve Bayes. The relationship between sentiment and financial health (Altman Z-Score) was analyzed using ordinal logistic regression. Results both social media sentiment (p=0.029) and news sentiment (p=0.042) significantly influence financial health. Positive sentiment on platform X shows the strongest predictive power (Exp(B)=3.907) in identifying "healthy" companies. Conclusion investment sentiment is a robust early warning indicator. Positive investor sentiment significantly reduces the probability of financial distress, offering a dynamic real-time monitoring tool for regulators and investors in a competitive market.
Co-Authors Nor ishah Mohd Ali Revina Sofia Septiani