Automatic construction of direction-aware sentiment lexicon using direction-dependent words

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Language Resources and Evaluation Pub Date : 2024-05-25 DOI:10.1007/s10579-024-09737-9
Jihye Park, Hye Jin Lee, Sungzoon Cho
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Abstract

Explainability, which is the degree to which an interested stakeholder can understand the key factors that led to a data-driven model’s decision, has been considered an essential consideration in the financial domain. Accordingly, lexicons that can achieve reasonable performance and provide clear explanations to users have been among the most popular resources in sentiment-based financial forecasting. Since deep learning-based techniques have limitations in that the basis for interpreting the results is unclear, lexicons have consistently attracted the community’s attention as a crucial tool in studies that demand explanations for the sentiment estimation process. One of the challenges in the construction of a financial sentiment lexicon is the domain-specific feature that the sentiment orientation of a word can change depending on the application of directional expressions. For instance, the word “cost” typically conveys a negative sentiment; however, when the word is juxtaposed with “decrease” to form the phrase “cost decrease,” the associated sentiment is positive. Several studies have manually built lexicons containing directional expressions. However, they have been hindered because manual inspection inevitably requires intensive human labor and time. In this study, we propose to automatically construct the “sentiment lexicon composed of direction-dependent words,” which expresses each term as a pair consisting of a directional word and a direction-dependent word. Experimental results show that the proposed sentiment lexicon yields enhanced classification performance, proving the effectiveness of our method for the automated construction of a direction-aware sentiment lexicon.

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利用方向依赖词自动构建方向感知情感词典
可解释性,即相关利益方能够理解导致数据驱动模型决策的关键因素的程度,一直被认为是金融领域的一个基本考虑因素。因此,能够实现合理性能并为用户提供清晰解释的词典一直是基于情感的金融预测中最受欢迎的资源之一。由于基于深度学习的技术有其局限性,即解释结果的依据不明确,因此在要求解释情感估计过程的研究中,词典作为一种重要工具一直吸引着社会各界的关注。构建金融情感词典所面临的挑战之一,是单词的情感取向会因定向表达的应用而改变这一特定领域的特征。例如,"成本 "一词通常传达的是负面情感;然而,当该词与 "减少 "并列构成短语 "成本减少 "时,相关情感则是正面的。有几项研究已经人工建立了包含定向表达的词典。然而,由于人工检查不可避免地需要大量的人力和时间,因此这些研究受到了阻碍。在本研究中,我们提出自动构建 "由方向依赖词组成的情感词库",该词库将每个术语表述为由一个方向词和一个方向依赖词组成的一对。实验结果表明,所提出的情感词典提高了分类性能,证明了我们的方法在自动构建方向感知情感词典方面的有效性。
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来源期刊
Language Resources and Evaluation
Language Resources and Evaluation 工程技术-计算机:跨学科应用
CiteScore
6.50
自引率
3.70%
发文量
55
审稿时长
>12 weeks
期刊介绍: Language Resources and Evaluation is the first publication devoted to the acquisition, creation, annotation, and use of language resources, together with methods for evaluation of resources, technologies, and applications. Language resources include language data and descriptions in machine readable form used to assist and augment language processing applications, such as written or spoken corpora and lexica, multimodal resources, grammars, terminology or domain specific databases and dictionaries, ontologies, multimedia databases, etc., as well as basic software tools for their acquisition, preparation, annotation, management, customization, and use. Evaluation of language resources concerns assessing the state-of-the-art for a given technology, comparing different approaches to a given problem, assessing the availability of resources and technologies for a given application, benchmarking, and assessing system usability and user satisfaction.
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