基于类别相关特征集的经济和财经新闻混合分类

IF 0.9 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligent Data Analysis Pub Date : 2023-11-30 DOI:10.3233/ida-237373
Wilawan Yathongkhum, Y. Laosiritaworn, Jakramate Bootkrajang, Pucktada Treeratpituk, Jeerayut Chaijaruwanich
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引用次数: 0

摘要

现在,人们可以通过各种新闻网站和社交媒体平台获取大量经济和金融新闻。将这些新闻归入适当的类别有利于开展各种任务,如情感分析和基于新闻的市场预测。遗憾的是,由于作者或出版商对标签分配的主观性,新闻标题类别可能存在模糊性。因此,实现精确的新闻分类不仅耗时,而且仍然依赖于人类的专业知识。为了解决这一具有挑战性的任务,我们提出了一种混合方法来提高经济和金融新闻分类的性能。这种方法将基准分类器与一种称为类别关联特征集(CAFS)分类器的新方法相结合。CAFS 将文本输入从词典空间转换到实体空间,并发现实体和类别之间的关联,类似于关联规则学习。在三个数据集上的实验结果表明,所提出的方法与现有方法不相上下,而且在域外数据集的分类结果上有显著改进。此外,将 CAFS 与现有的文本分类基线结合使用,可以提供一种通用分类器,用于区分不同来源的新闻类别,而无需对这些分类基线的相关参数进行大量微调。这证实了在混合方法中使用 CAFS 是合适的,适合于经济和金融新闻分类。
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Economic and financial news hybrid- classification based on category-associated feature set
A large amount of economic and financial news is now accessible through various news websites and social media platforms. Categorizing them into appropriate categories can be advantageous for various tasks, such as sentiment analysis and news-based market prediction. Unfortunately, news headlines categories may contain ambiguities due to the subjective nature of label assignment by authors or publishers. Consequently, achieving precise classification of news can be time-consuming and still reliant on human expertise. To tackle this challenging task, we proposed a hybrid approach to enhance the performance of economic and financial news classification. This approach combines baseline classifiers with a novel method called the Category Associated Feature Set (CAFS) classifier. CAFS transforms text input from the lexicon-space into the entity-space and discovers associations between entities and classes, akin to association rule learning. Experimental results on three datasets demonstrated that the proposed method is comparable to existing approaches and exhibits a significant improvement in the classification results for out-of-domain datasets. Additionally, employing CAFS in tandem with the existing text classification baselines can provide a general categorizer for distinguishing news categories across various sources without the need for extensive fine-tuning of the parameters associated with those classification baselines. This confirms that utilizing CAFS in a hybrid approach is appropriate and suitable for economic and financial news classification.
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来源期刊
Intelligent Data Analysis
Intelligent Data Analysis 工程技术-计算机:人工智能
CiteScore
2.20
自引率
5.90%
发文量
85
审稿时长
3.3 months
期刊介绍: Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.
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