NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Kee**
Abstract
:1. Introduction
- Detecting Euphemisms and Metaphors [20]: Concealment can sometimes occur through euphemisms or metaphors. NLP techniques that recognise semantic relations can identify these linguistic phenomena, although deciphering their exact meaning may still be complex.
- Analysing Sentiment [21]: By examining the sentiment of a text, NLP might identify inconsistencies or subtle cues that something might be hidden. For instance, if the sentiment within a document suddenly changes without apparent reason, it may indicate an attempt to conceal information.
- Steganography Detection [22]: Steganography is hiding information within other information. Although commonly associated with images, it can also be applied to text. Specialised algorithms can sometimes detect patterns in text that might indicate steganography.
- Identifying Non-Natural Language Patterns [23]: If information is concealed through coded language or special patterns, advanced NLP techniques may detect the non-natural use of language. By modelling what is considered ‘typical’ language usage, deviations from this norm can be flagged for further investigation.
- Using Contextual Analysis [24]: Sometimes, what is not said is as important as what is said. Analysing the context in which information is presented and cross-referencing it with known facts can reveal inconsistencies that might indicate concealment.
- Challenges and Limitations [25]: Recognising that detecting concealed information is a highly complex task is crucial. It may require domain-specific knowledge and careful tuning of algorithms. Furthermore, false positives are possible, where the algorithms mistakenly identify concealed information where there is none.
- Legal and Ethical Considerations [26]: These techniques must also be aligned with legal and ethical standards, especially concerning privacy and consent.
2. Related Work in the Literature
- -
- Analysing financial ratios: investigating key financial ratios and metrics to identify inconsistencies or anomalies.
- -
- Comparing with industry benchmarks: comparing the company’s financial performance with industry standards and peers.
- -
- Reviewing auditor’s statements: analysing the auditor’s comments and any qualifications in the report.
- -
- Using specialised tools: employing specialised forensic accounting tools and techniques to detect potential fraud.
4. Results
4.1. Wirecard
- ▪
- Polarity: A polarity score of 0.0674 indicates a slightly positive sentiment in the text. It may reflect optimistic language to present positive company performance and growth aspects.
- ▪
- Subjectivity: The subjectivity score of 0.3525 represents moderate subjectivity in the text. This score suggests a combination of objective financial data and subjective interpretations or opinions.
- ▪
- Polarity: A polarity score of 0.0697 indicates a slightly positive sentiment in the text. It aligns with typical financial reports, where positive language may emphasise growth or favourable performance.
- ▪
- Subjectivity: The subjectivity score of 0.3656 reflects moderate subjectivity in the text. It may indicate a combination of objective financial data and subjective interpretations or opinions.
- ▪
- Polarity: With a score of 0.0745, the document exhibits a slightly positive sentiment. It aligns with using positive language in financial reports to emphasise growth, success, or favourable conditions.
- ▪
- Subjectivity: The subjectivity score of 0.3661 indicates moderate subjectivity in the text, reflecting a mix of objective financial data and subjective interpretations or opinions.
- ▪
- Polarity: This score indicates a slightly positive sentiment in the text. A score closer to 1 would signify a strong positive sentiment, while a score closer to −1 would indicate a strong negative sentiment. The given score suggests a mildly positive tone in the document.
- ▪
- Subjectivity: This score reflects a moderate level of subjectivity in the text. A score closer to 1 would indicate high subjectivity (personal opinions or feelings), while a score closer to 0 would indicate objectivity (factual information).
- ▪
- Polarity: This score ranges from −1 to 1, where −1 indicates a negative sentiment, 1 indicates a positive sentiment, and 0 indicates a neutral sentiment. The score of 0.0716 suggests a slightly positive sentiment in the text.
- ▪
- Subjectivity: This score ranges from 0 to 1, where 0 is objective and 1 is subjective. A score of 0.3807 indicates a moderate level of subjectivity in the text.
4.2. Tesco
- ▪
- Polarity: A polarity score of 0.1071 indicates a slightly positive sentiment in the text. It is consistent with typical financial reports, where positive language may be used to present an optimistic view of the company’s performance.
- ▪
- Subjectivity: The subjectivity score of 0.3715 represents moderate subjectivity in the text. It may suggest a mix of objective financial data and subjective interpretations or opinions.
- ▪
- Polarity: A polarity score of 0.1018 indicates a slightly positive sentiment in the text. It may reflect the positive language used in annual financial reports to convey success, growth, or achievements.
- ▪
- Subjectivity: The subjectivity score of 0.3649 represents moderate subjectivity in the text. This score suggests a blend of objective financial data and subjective interpretations or opinions.
- ▪
- Polarity: A polarity score of 0.1186 indicates a positive sentiment in the text. It is consistent with typical financial reports, where positive language may convey an optimistic view of the company’s performance and achievements.
- ▪
- Subjectivity: The subjectivity score of 0.5984 is higher than previous reports, reflecting a substantial level of subjectivity in the text. It may indicate the presence of more opinions, interpretations, or subjective statements in addition to the objective financial data.
- ▪
- Polarity: A polarity score of 0.1479 indicates a positive sentiment in the text. It suggests that the document contains language emphasising positive aspects, achievements, or favourable conditions. It is a common practice in annual reports to convey an optimistic view of the company’s performance.
- ▪
- Subjectivity: The subjectivity score of 0.4607 reflects a higher subjectivity level than previous reports. It may indicate the presence of more opinions, interpretations, or subjective statements in the text in addition to the objective financial data.
4.3. Under Armour
- ▪
- Polarity: A polarity score of 0.0506 indicates a slightly positive sentiment in the text. It likely reflects the language used to emphasise Under Armour’s commitment to innovation, athlete insights, and product development.
- ▪
- Subjectivity: The subjectivity score of 0.3592 represents moderate subjectivity in the text. It suggests a blend of objective data and subjective interpretations or opinions.
- ▪
- Polarity: A polarity score of 0.0507 indicates a slightly positive sentiment in the text. It reflects the language used to present the brand’s global expansion, product launches, and plans for growth in a positive light.
- ▪
- Subjectivity: The subjectivity score of 0.3622 represents moderate subjectivity in the text. It may suggest a combination of objective facts and subjective interpretations or opinions.
- ▪
- Polarity: A polarity score of 0.0353 indicates a slightly positive sentiment in the text. It may reflect the language used to highlight achievements, endorsements, and growth in the brand’s connected fitness systems.
- ▪
- Subjectivity: The subjectivity score of 0.3612 represents moderate subjectivity in the text. It suggests a mix of factual financial information and subjective expressions or opinions.
- ▪
- Polarity: A polarity score of 0.0407 indicates a slightly positive sentiment in the text. It may reflect the language used to convey success, awards, and positive achievements related to the brand’s performance in the footwear industry.
- ▪
- Subjectivity: The subjectivity score of 0.3655 represents moderate subjectivity in the text. It may suggest a combination of factual data and subjective expressions or interpretations.
- ▪
- Polarity: A polarity score of 0.0446 indicates a slightly positive sentiment in the text. It suggests the document contains language emphasising positive aspects, achievements, or favourable conditions. It is common for annual reports to convey a positive view of the company’s performance.
- ▪
- Subjectivity: The subjectivity score of 0.3534 reflects moderate subjectivity in the text. It may indicate a blend of objective data and subjective statements or interpretations.
5. Discussion
- ▪
- Wirecard: Known for fraudulent financial statements, the analysis revealed a consistent tone across the years, with a slight decrease in polarity and subjectivity in 2018.
- ▪
- Tesco: With fraud related to the year 2014, Tesco exhibited a marked increase in polarity and subjectivity during the fraud year.
- ▪
- Under Armour: During the fraud years of 2015–2016, Under Armour showed a decrease in polarity, while subjectivity remained moderately consistent.
Implications and Limitations
6. Conclusions
Future Research
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Tonkiss, F. Trust, confidence and economic crisis. Intereconomics 2009, 44, 196–202. [Google Scholar] [CrossRef]
- Fung, B. The demand and need for transparency and disclosure in corporate governance. Univers. J. Manag. 2014, 2, 72–80. [Google Scholar] [CrossRef]
- Gardi, B.; Abdalla Hamza, P.; Sabir, B.Y.; Mahmood Aziz, H.; Sorguli, S.; Abdullah, N.N.; Al-Kake, F. Investigating the effects of financial accounting reports on managerial decision-making in small and medium-sized enterprises. Turk. J. Comput. Math. Educ. 2021, 12, 2134–2142. [Google Scholar] [CrossRef]
- Currie, W.L.; Gozman, D.P.; Seddon, J.J. Dialectic tensions in the financial markets: A longitudinal study of pre-and post-crisis regulatory technology. J. Inf. Technol. 2018, 33, 304–325. [Google Scholar] [CrossRef]
- Rezaee, Z. Corporate Governance and Ethics; John Wiley & Sons: Hoboken, NJ, USA, 2008. [Google Scholar]
- Rezaee, Z. Causes, consequences, and deterrence of financial statement fraud. Crit. Perspect. Account. 2005, 16, 277–298. [Google Scholar] [CrossRef]
- Triani, N. Fraudulent Financial Reporting Detection Using Beneish M-Score Model in Public Companies in 2012-2016. Asia Pac. Fraud J. 2019, 4, 27–42. [Google Scholar]
- Anderson, J.R.; Tirrell, M.E. Too Good to Be True CEOs and Financial Reporting Fraud. Consult. Psychol. J. Pract. Res. 2004, 56, 35. [Google Scholar] [CrossRef]
- Tirole, J. The Theory of Corporate Finance; Princeton University Press: Princeton, NJ, USA, 2010. [Google Scholar]
- Asare, S.K.; Wright, A.; Zimbelman, M.F. Challenges facing auditors in detecting financial statement fraud: Insights from fraud investigations. J. Forensic Investig. Account. 2015, 7, 63–111. [Google Scholar]
- Rezaee, Z. Financial Statement Fraud: Prevention and Detection; John Wiley & Sons: Hoboken, NJ, USA, 2002. [Google Scholar]
- Segal, S. Accounting frauds–review of advanced technologies to detect and prevent frauds. Econ. Bus. Rev. 2016, 2, 45–64. [Google Scholar] [CrossRef]
- Dorminey, J.; Fleming, A.S.; Kranacher, M.J.; Riley, R.A., Jr. The evolution of fraud theory. Issues Account. Educ. 2012, 27, 555–579. [Google Scholar] [CrossRef]
- Craja, P.; Kim, A.; Lessmann, S. Deep learning for detecting financial statement fraud. Decis. Support Syst. 2020, 139, 113421. [Google Scholar] [CrossRef]
- Leuz, C.; Wysocki, P.D. Economic Consequences of Financial Reporting and Disclosure Regulation: A Review and Suggestions for Future Research. 2008. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1105398 (accessed on 10 March 2021).
- Roychowdhury, S.; Shroff, N.; Verdi, R.S. The effects of financial reporting and disclosure on corporate investment: A review. J. Account. Econ. 2019, 68, 101246. [Google Scholar] [CrossRef]
- Luitel, D. A Language-Model-Based Approach for Detecting Incompleteness in Natural-Language Requirements. Ph.D. Dissertation, University of Ottawa, Ottawa, ON, Canada, 2023. [Google Scholar]
- Kang, Y.; Cai, Z.; Tan, C.W.; Huang, Q.; Liu, H. Natural language processing (NLP) in management research: A literature review. J. Manag. Anal. 2020, 7, 139–172. [Google Scholar] [CrossRef]
- Lewis, D.D.; Jones, K.S. Natural language processing for information retrieval. Commun. ACM 1996, 39, 92–101. [Google Scholar] [CrossRef]
- Felt, C.; Riloff, E. Recognising euphemisms and dysphemisms using sentiment analysis. In Proceedings of the Second Workshop on Figurative Language Processing, Online, July 2020; pp. 136–145. [Google Scholar]
- Goel, S.; Uzuner, O. Do sentiments matter in fraud detection? Estimating semantic orientation of annual reports. Intell. Syst. Account. Financ. Manag. 2016, 23, 215–239. [Google Scholar] [CrossRef]
- Chen, Z.; Huang, L.; Yu, Z.; Yang, W.; Li, L.; Zheng, X.; Zhao, X. Linguistic Steganography Detection Using Statistical Characteristics of Correlations between Words. In Proceedings of the Information Hiding: 10th International Workshop, IH 2008, Santa Barbara, CA, USA, 19–21 May 2008; Revised Selected Papers 10; Springer: Berlin/Heidelberg, Germany, 2008; pp. 224–235. [Google Scholar]
- Hirsch, T.; Hofer, B. Detecting non-natural language artifacts for de-noising bug reports. Autom. Softw. Eng. 2022, 29, 52. [Google Scholar] [CrossRef] [PubMed]
- Iqbal, S.; Hassan, S.U.; Aljohani, N.R.; Alelyani, S.; Nawaz, R.; Bornmann, L. A decade of in-text citation analysis based on natural language processing and machine learning techniques: An overview of empirical studies. Scientometrics 2021, 126, 6551–6599. [Google Scholar] [CrossRef]
- Caldarini, G.; Jaf, S.; McGarry, K. A literature survey of recent advances in chatbots. Information 2022, 13, 41. [Google Scholar] [CrossRef]
- Rogers, A.; Baldwin, T.; Leins, K. Just What do You Think You’re Doing, Dave?’ A Checklist for Responsible Data Use in NLP. ar**v 2021, ar**v:2109.06598. [Google Scholar]
- Aghili, S. Fraud Auditing Using CAATT: A Manual for Auditors and Forensic Accountants to Detect Organisational Fraud; CRC Press: Boca Raton, FL, USA, 2019. [Google Scholar]
- Huang, B.; Guo, R.; Zhu, Y.; Fang, Z.; Zeng, G.; Liu, J.; Wang, Y.; Fujita, H.; Shi, Z. Aspect-level sentiment analysis with aspect-specific context position information. Knowl. -Based Syst. 2022, 243, 108473. [Google Scholar] [CrossRef]
- Fei, H.; Zhang, Y.; Ren, Y.; Ji, D. Latent Emotion Memory for Multi-Label Emotion Classification. In Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; Volume 34, pp. 7692–7699. [Google Scholar]
- Fei, H.; Li, F.; Li, C.; Wu, S.; Li, J.; Ji, D. Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-Based Sentiment Analysis. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI, Vienna, Austria, 23–29 July 2022; pp. 4096–4103. [Google Scholar]
- Sohangir, S.; Wang, D.; Pomeranets, A.; Khoshgoftaar, T.M. Big Data: Deep Learning for financial sentiment analysis. J. Big Data 2018, 5, 3. [Google Scholar] [CrossRef]
- **ang, C.; Zhang, J.; Li, F.; Fei, H.; Ji, D. A semantic and syntactic enhanced neural model for financial sentiment analysis. Inf. Process. Manag. 2022, 59, 102943. [Google Scholar] [CrossRef]
- Shang, L.; **, H.; Hua, J.; Tang, H.; Zhou, J. A Lexicon Enhanced Collaborative Network for targeted financial sentiment analysis. Inf. Process. Manag. 2023, 60, 103187. [Google Scholar] [CrossRef]
- Faccia, A.; Petratos, P. NLP and IR Applications for Financial Reporting and Non-Financial Disclosure. Framework Implementation and Roadmap for Feasible Integration with the Accounting Process. In Proceedings of the 6th International Conference on Natural Language Processing and Information Retrieval, Bangkok, Thailand, 16–18 December 2022; pp. 117–124. [Google Scholar]
- Faccia, A.; Mosteanu, N.R. Accounting and blockchain technology: From double-entry to triple-entry. Bus. Manag. Rev. 2019, 10, 108–116. [Google Scholar]
- Mosteanu, N.R.; Faccia, A. Digital systems and new challenges of financial management–FinTech, XBRL, blockchain and cryptocurrencies. Qual.-Access Success 2020, 21, 159–166. [Google Scholar]
- Faccia, A.; Petratos, P. Blockchain, enterprise resource planning (ERP) and accounting information systems (AIS): Research on e-procurement and system integration. Appl. Sci. 2021, 11, 6792. [Google Scholar] [CrossRef]
- Faccia, A.; Mosteanu, N.R. Tax evasion, information systems and blockchain. J. Inf. Syst. Oper. Manag. 2019, 13, 65–74. [Google Scholar]
- Mosteanu, N.R.; Faccia, A. Fintech frontiers in quantum computing, fractals, and blockchain distributed ledger: Paradigm shifts and open innovation. J. Open Innov. Technol. Mark. Complex. 2021, 7, 19. [Google Scholar] [CrossRef]
- Tetlock, P.E. Cognitive biases and organizational correctives: Do both disease and cure depend on the politics of the beholder? Adm. Sci. Q. 2000, 45, 293–326. [Google Scholar] [CrossRef]
- Langevoort, D.C. Disasters and Disclosures: Securities Fraud Liability in the Shadow of a Corporate Catastrophe. Geo. LJ 2018, 107, 967. [Google Scholar]
- French, S.; Maule, J.; Papamichail, N. Decision Behaviour, Analysis and Support; Cambridge University Press: Cambridge, UK, 2009. [Google Scholar]
- Hirshleifer, D. Psychological bias as a driver of financial regulation. Eur. Financ. Manag. 2008, 14, 856–874. [Google Scholar] [CrossRef]
- Raval, V. A disposition-based fraud model: Theoretical integration and research agenda. J. Bus. Ethics 2018, 150, 741–763. [Google Scholar] [CrossRef]
- Repenning, N.; Löhlein, L.; Schäffer, U. Emotions in accounting: A review to bridge the paradigmatic divide. Eur. Account. Rev. 2022, 31, 241–267. [Google Scholar] [CrossRef]
- Faccia, A.; Al Naqbi, M.Y.K.; Lootah, S.A. August. Integrated Cloud Financial Accounting Cycle: How Artificial Intelligence, Blockchain, and XBRL Will Change the Accounting, Fiscal and Auditing Practices. In Proceedings of the 2019 3rd International Conference on Cloud and Big Data Computing, Oxford, UK, 28–30 August 2019; pp. 31–37. [Google Scholar]
- Faccia, A.; Pandey, V.; Banga, C. Is permissioned blockchain the key to support the external audit shift to entirely open innovation paradigm? J. Open Innov. Technol. Mark. Complex. 2022, 8, 85. [Google Scholar] [CrossRef]
- Petratos, P.N.; Faccia, A. Fake news, misinformation, disinformation and supply chain risks and disruptions: Risk management and resilience using blockchain. Ann. Oper. Res. 2023, 327, 735–762. [Google Scholar] [CrossRef]
- Armstrong, C.S.; Guay, W.R.; Weber, J.P. The role of information and financial reporting in corporate governance and debt contracting. J. Account. Econ. 2010, 50, 179–234. [Google Scholar] [CrossRef]
- Doyle, J.; Ge, W.; McVay, S. Determinants of weaknesses in internal control over financial reporting. J. Account. Econ. 2007, 44, 193–223. [Google Scholar] [CrossRef]
- Dalnial, H.; Kamaluddin, A.; Sanusi, Z.M.; Khairuddin, K.S. Accountability in financial reporting: Detecting fraudulent firms. Procedia-Soc. Behav. Sci. 2014, 145, 61–69. [Google Scholar] [CrossRef]
- Zainudin, E.F.; Hashim, H.A. Detecting fraudulent financial reporting using financial ratio. J. Financ. Report. Account. 2016, 14, 266–278. [Google Scholar] [CrossRef]
- Ho, J.; Cheng, W. Metaphors in financial analysis reports: How are emotions expressed? Engl. Specif. Purp. 2016, 43, 37–48. [Google Scholar] [CrossRef]
- Türegün, N. Text mining in financial information. Curr. Anal. Econ. Financ. 2019, 1, 18–26. [Google Scholar]
- El-Haj, M.; Rayson, P.; Walker, M.; Young, S.; Simaki, V. In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse. J. Bus. Financ. Account. 2019, 46, 265–306. [Google Scholar] [CrossRef]
- Busk, P.L.; Serlin, R.C. Meta-Analysis for Single-Case Research. In Single-Case Research Design and Analysis (Psychology Revivals); Routledge: Oxfordshire, UK, 2015; pp. 187–212. [Google Scholar]
- Bolton, R.J.; Hand, D.J. Statistical fraud detection: A review. Stat. Sci. 2002, 17, 235–255. [Google Scholar] [CrossRef]
- Ras, I.A. A Corpus-Assisted Critical Discourse Analysis of the Reporting on Corporate Fraud by UK Newspapers 2004–2014. Ph.D. Dissertation, University of Leeds, Leeds, UK, 2017. [Google Scholar]
- Neu, D.; Warsame, H.; Pedwell, K. Managing public impressions: Environmental disclosures in annual reports. Account. Organ. Soc. 1998, 23, 265–282. [Google Scholar] [CrossRef]
- Boiral, O. Sustainability reports as simulacra? A counter-account of A and A+ GRI reports. Account. Audit. Account. J. 2013, 26, 1036–1071. [Google Scholar] [CrossRef]
- Appel, O.; Chiclana, F.; Carter, J.; Fujita, H. A hybrid approach to the sentiment analysis problem at the sentence level. Knowl.-Based Syst. 2016, 108, 110–124. [Google Scholar] [CrossRef]
- Poria, S.; Cambria, E.; Winterstein, G.; Huang, G.B. Sentic patterns: Dependency-based rules for concept-level sentiment analysis. Knowl.-Based Syst. 2014, 69, 45–63. [Google Scholar] [CrossRef]
- Nasukawa, T.; Yi, J. Sentiment Analysis: Capturing Favorability Using Natural Language Processing. In Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island, FL, USA, 23–25 October 2003; pp. 70–77. [Google Scholar]
- Zucco, C.; Calabrese, B.; Agapito, G.; Guzzi, P.H.; Cannataro, M. Sentiment analysis for mining texts and social networks data: Methods and tools. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020, 10, e1333. [Google Scholar] [CrossRef]
- Zhang, L.; Wang, S.; Liu, B. Deep learning for sentiment analysis: A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2018, 8, e1253. [Google Scholar] [CrossRef]
- Bengfort, B.; Bilbro, R.; Ojeda, T. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning; O’Reilly Media, Inc.: Sebastopol, CA, USA, 2018. [Google Scholar]
- Simperl, E. Reusing ontologies on the Semantic Web: A feasibility study. Data Knowl. Eng. 2009, 68, 905–925. [Google Scholar] [CrossRef]
- Zhang, Y.; Sidibé, D.; Morel, O.; Mériaudeau, F. Deep multimodal fusion for semantic image segmentation: A survey. Image Vis. Comput. 2021, 105, 104042. [Google Scholar] [CrossRef]
- Zhu, X.; Ao, X.; Qin, Z.; Chang, Y.; Liu, Y.; He, Q.; Li, J. Intelligent financial fraud detection practices in post-pandemic era. Innov. 2021, 2, 100176. [Google Scholar] [CrossRef] [PubMed]
- Harner, M.M. Barriers to effective risk management. Seton Hall L. Rev. 2010, 40, 1323. [Google Scholar]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Faccia, A.; McDonald, J.; George, B. NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Kee**. Computers 2024, 13, 5. https://doi.org/10.3390/computers13010005
Faccia A, McDonald J, George B. NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Kee**. Computers. 2024; 13(1):5. https://doi.org/10.3390/computers13010005
Chicago/Turabian StyleFaccia, Alessio, Julie McDonald, and Babu George. 2024. "NLP Sentiment Analysis and Accounting Transparency: A New Era of Financial Record Kee**" Computers 13, no. 1: 5. https://doi.org/10.3390/computers13010005