利用在线新闻分类检测印度尼西亚 GDP 变动情况

Dinda Pusparahmi Sholawatunnisa, L. H. Suadaa, Usep Nugraha, Setia Pramana
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引用次数: 0

摘要

国内生产总值(GDP)是一个关键指标,可提供经济动态的战略洞察力。最近的技术进步,尤其是通过在线经济新闻平台进行实时信息传播的技术进步,为分析国内生产总值的变动提供了一个可访问的替代数据源。本研究利用在线新闻分类来识别印尼国内生产总值的变动和增长率模式。我们利用网络搜索技术收集数据进行分析。采用的分类模型包括来自预训练语言模型转换器的迁移学习,以及作为基准模型的经典机器学习方法。结果表明,预先训练的语言模型转换器性能优越,达到了 0.8880 和 0.7899 的最高准确率。相比之下,经过超参数调整的经典机器学习模型也取得了可喜的成绩,最佳准确率分别达到了 0.845 和 0.7811。这项研究强调了利用网络新闻分类的有效性,特别是通过先进的语言模型。研究结果有助于深入理解经济动态,与当代信息可获取性和技术进步相一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indonesian GDP movement detection using online news classification
Gross Domestic Product (GDP) stands as a pivotal indicator, offering strategic insights into economic dynamics. Recent technological advancements, particularly in real-time information dissemination through online economic news platforms, provide an accessible and alternative data source for analyzing GDP movements. This study employs online news classification to identify patterns in the movement and growth rate of Indonesia’s GDP. Utilizing a web scraping technique, we collected data for analysis. The classification models employed include transfer learning from pre-trained language model transformers, with classical machine learning methods serving as baseline models. The results indicate superior performance by the pre-trained language model transformers, achieving the highest accuracy of 0.8880 and 0.7899. In comparison, hyperparameter-tuned classical machine learning models also demonstrated commendable results, with the best accuracy reaching 0.845 and 0.7811. This research underscores the efficacy of leveraging online news classification, particularly through advanced language models. The findings contribute to a nuanced understanding of economic dynamics, aligning with the contemporary landscape of information accessibility and technological progress.
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