WGSRN: Data Collection & Empirical Methods (Topic)最新文献

Fine-Grained, Aspect-Based Sentiment Analysis on Economic and Financial Lexicon 经济金融词汇的细粒度、面向方面的情感分析

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2021-01-14 DOI: 10.2139/ssrn.3766194

S. Consoli, Luca Barbaglia, S. Manzan

{"title":"Fine-Grained, Aspect-Based Sentiment Analysis on Economic and Financial Lexicon","authors":"S. Consoli, Luca Barbaglia, S. Manzan","doi":"10.2139/ssrn.3766194","DOIUrl":"https://doi.org/10.2139/ssrn.3766194","url":null,"abstract":"The last two decades have seen a tremendous increase in the adoption of Semantic Web technologies as a result of the availability of big data, the growth in computational power and the advancement of artificial intelligence (AI) technologies. Cutting-edge semantic techniques are now able to capture sentiments more accurately in various practical applications, including economic and financial forecasting. In particular, the extraction of sentiment from news text, social media and blogs for the prediction of economic and financial variables has attracted attention in recent years. Despite many successful applications of sentiment analysis (SA) in these domains, the range of semantic techniques employed is still limited and mostly focused on the detection of sentiment at a coarse-grained level, that is, whether the sentiment expressed by the entire text of a sentence is either positive or negative. This paper proposes a novel methodology for Fine-Grained Aspect-based Sentiment (FiGAS) analysis. The aim of the approach is to identify the sentiment associated to specific topics of interest in each sentence of a document and assigning real-valued polarity scores between -1 and +1 to those topics. The proposed approach is completely unsupervised and customized to the economic and financial domains by using a specialized lexicon make available along with the source code of FiGAS. Our lexicon-based SA approach relies on a detailed set of semantic polarity rules that allow understanding the origin of sentiment, in the spirit of the recent trend on textit{Interpretable AI}. We provide an in-depth comparison of the performance of the FiGAS algorithm relative to other popular lexicon-based SA approaches in predicting a humanly annotated data set in the economic and financial domains. Our results indicate that FiGAS statistically outperforms the other methods by providing a sentiment score that is closer to one of the human annotators.","PeriodicalId":331527,"journal":{"name":"WGSRN: Data Collection & Empirical Methods (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127809110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases 比较传统和机器学习方法在家庭暴力案件中的风险评估

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2020-12-01 DOI: 10.2139/ssrn.3760094

J. Grogger, Sean Gupta, Ria Ivandić, Tom Kirchmaier

引用次数: 23

The Pleasant Yield of Financial Illiteracy 金融文盲带来的好处

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2020-11-17 DOI: 10.2139/ssrn.3732260

Yosef Bonaparte

引用次数: 0

Robots, Computers, and the Gender Wage Gap 机器人、计算机和性别工资差距

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2020-04-29 DOI: 10.2139/ssrn.3450162

Suqin Ge, Yu Zhou

引用次数: 20

Complex Information and Accounting Standards: Evidence from UK Narratives 复杂信息和会计准则:来自英国叙事的证据

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2019-07-31 DOI: 10.2139/ssrn.3429610

E. Efretuei, A. Usoro, Christina Koutra

{"title":"Complex Information and Accounting Standards: Evidence from UK Narratives","authors":"E. Efretuei, A. Usoro, Christina Koutra","doi":"10.2139/ssrn.3429610","DOIUrl":"https://doi.org/10.2139/ssrn.3429610","url":null,"abstract":"Accounting literature has documented the occurrence of complex accounting narratives and economic consequences such as market delays in extracting information from narratives. International Financial Reporting Standards (IFRS) were introduced to enhance reporting efficiencies and improve communication in financial reporting. However, research shows that the introduction of IFRS has increased narrative complexity, attributable to the demand for more reporting. Considering that accounting complexity can either be informative or non-informative, thereby causing obfuscation, this study performs an empirical analysis to explain the complexities in accounting narratives that may be affected by IFRS application. Using the framework and setting of IFRS adoption and a word list adjusted component of the fog index, it decomposes complexity into two components: information (common complexity) and obfuscation (uncommon complexity). The results evidence that IFRS adoption has increased the common complexity of accounting narratives that investors are likely to understand (information), but it does not necessarily increase obfuscation. Complementing the wider evidence around IFRS benefits, this evidence can be applied in accounting narrative complexity studies. This complexity decomposition is novel and contributes to the debate on the use of the fog index, while demonstrating the increased narrative comparability of IFRS reports.","PeriodicalId":331527,"journal":{"name":"WGSRN: Data Collection & Empirical Methods (Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126338896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gender Robustness of Overconfidence and Excess Entry 过度自信与过度进入的性别稳健性

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2019-03-26 DOI: 10.2139/ssrn.3189133

Katarína Danková, Maroš Servátka

引用次数: 10

Reaching the Top or Falling Behind?: The Role of Occupational Segregation in Women's Chances of Finding a High-Paying Job Over the Life-Cycle 达到顶峰还是落后?职业隔离在妇女一生中找到高薪工作机会中的作用

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2018-10-29 DOI: 10.2139/ssrn.3274877

Federico H. Gutierrez

引用次数: 0

News-Based Indices on Country Fundamentals 基于新闻的国家基本面指数

WGSRN: Data Collection & Empirical Methods (Topic) Pub Date : 2018-02-27 DOI: 10.2139/ssrn.3132278

Andras Fulop, Z. Kocsis

引用次数: 0