多语言文本分类和情感分析:使用多语言方法对推特数据进行分类的比较分析。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
George Manias, Argyro Mavrogiorgou, Athanasios Kiourtis, Chrysostomos Symvoulidis, Dimosthenis Kyriazis
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引用次数: 5

摘要

文本分类和情感分析是两种最典型的自然语言处理任务,在医疗保健和政策制定等不同领域实现和利用了各种新兴的应用程序。与此同时,推特等社交媒体的受欢迎程度和使用率的巨大增长,导致用户生成的数据大幅增加,主要表现为用户帖子中的相应文本。然而,由于这些数据的领域多样性和高度的多语性,分析这些具体数据并从中提取可操作的知识和附加值是一项具有挑战性的任务。后者强调了实施和利用领域不可知和多语言解决方案的新需求。为了调查其中的一部分挑战,本研究工作对用于对所检查的多语言语料库的情感和文本进行分类的多语言方法进行了比较分析。在此背景下,使用并比较了四种基于BERT的多语言分类器和零样本分类方法在多语言数据分类中的准确性和适用性。他们的比较揭示了深刻的结果,并有双重解释。当对多语言数据进行训练和微调时,基于多语言BERT的分类器实现了高性能和转移推理。同时,零样本方法提供了一种新颖的技术,可以以更快、更高效和可扩展的方式创建多语言解决方案。它可以很容易地适应新语言和新任务,同时在许多语言中取得相对良好的结果。然而,当效率和可扩展性不如准确性重要时,该模型以及通常的零样本模型似乎无法与经过微调和训练的基于BERT的多语言分类器相比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data.

Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data.

Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data.

Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data.

Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users' posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data. The latter highlights the emerging need for the implementation and utilization of domain-agnostic and multilingual solutions. To investigate a portion of these challenges this research work performs a comparative analysis of multilingual approaches for classifying both the sentiment and the text of an examined multilingual corpus. In this context, four multilingual BERT-based classifiers and a zero-shot classification approach are utilized and compared in terms of their accuracy and applicability in the classification of multilingual data. Their comparison has unveiled insightful outcomes and has a twofold interpretation. Multilingual BERT-based classifiers achieve high performances and transfer inference when trained and fine-tuned on multilingual data. While also the zero-shot approach presents a novel technique for creating multilingual solutions in a faster, more efficient, and scalable way. It can easily be fitted to new languages and new tasks while achieving relatively good results across many languages. However, when efficiency and scalability are less important than accuracy, it seems that this model, and zero-shot models in general, can not be compared to fine-tuned and trained multilingual BERT-based classifiers.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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