{"title":"Automatic construction of direction-aware sentiment lexicon using direction-dependent words","authors":"Jihye Park, Hye Jin Lee, Sungzoon Cho","doi":"10.1007/s10579-024-09737-9","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":49927,"journal":{"name":"Language Resources and Evaluation","volume":"63 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Language Resources and Evaluation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10579-024-09737-9","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
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.