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Learning tone and attribution for financial text mining

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Learning tone and attribution for financial text mining. / El-Haj, Mahmoud; Rayson, Paul Edward; Young, Steven Eric; Walker, Martin; Moore, Andrew; Athanasakou, Vasiliki; Schleicher, Thomas.

Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation. ed. / Nicoletta Calzolari; Khalid Choukri; Thierry Declerck; Marko Grobelnik; Bente Maegaard; Joseph Mariani; Asuncion Moreno; Jan Odijk; Stelios Piperidis. European Language Resources Association (ELRA), 2016. p. 1820-1825.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

El-Haj, M, Rayson, PE, Young, SE, Walker, M, Moore, A, Athanasakou, V & Schleicher, T 2016, Learning tone and attribution for financial text mining. in N Calzolari, K Choukri, T Declerck, M Grobelnik, B Maegaard, J Mariani, A Moreno, J Odijk & S Piperidis (eds), Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA), pp. 1820-1825.

APA

El-Haj, M., Rayson, P. E., Young, S. E., Walker, M., Moore, A., Athanasakou, V., & Schleicher, T. (2016). Learning tone and attribution for financial text mining. In N. Calzolari, K. Choukri, T. Declerck, M. Grobelnik, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, ... S. Piperidis (Eds.), Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation (pp. 1820-1825). European Language Resources Association (ELRA).

Vancouver

El-Haj M, Rayson PE, Young SE, Walker M, Moore A, Athanasakou V et al. Learning tone and attribution for financial text mining. In Calzolari N, Choukri K, Declerck T, Grobelnik M, Maegaard B, Mariani J, Moreno A, Odijk J, Piperidis S, editors, Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation. European Language Resources Association (ELRA). 2016. p. 1820-1825

Author

El-Haj, Mahmoud ; Rayson, Paul Edward ; Young, Steven Eric ; Walker, Martin ; Moore, Andrew ; Athanasakou, Vasiliki ; Schleicher, Thomas. / Learning tone and attribution for financial text mining. Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation. editor / Nicoletta Calzolari ; Khalid Choukri ; Thierry Declerck ; Marko Grobelnik ; Bente Maegaard ; Joseph Mariani ; Asuncion Moreno ; Jan Odijk ; Stelios Piperidis. European Language Resources Association (ELRA), 2016. pp. 1820-1825

Bibtex

@inproceedings{ebf357668541405f8461fcf4ac4fd1cd,
title = "Learning tone and attribution for financial text mining",
abstract = "Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds ofattributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale.Our best machine learning algorithm correctly classified performance sentences with 70{\%} accuracy and detected tone and attribution in financial PEAs with accuracy of 79{\%}.",
keywords = "NLP, Machine Learning, Financial narratives, financial documents, PEA, Text Analysis, Natural Language Processing",
author = "Mahmoud El-Haj and Rayson, {Paul Edward} and Young, {Steven Eric} and Martin Walker and Andrew Moore and Vasiliki Athanasakou and Thomas Schleicher",
year = "2016",
month = "5",
day = "23",
language = "English",
isbn = "9782951740891",
pages = "1820--1825",
editor = "Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis",
booktitle = "Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation",
publisher = "European Language Resources Association (ELRA)",

}

RIS

TY - GEN

T1 - Learning tone and attribution for financial text mining

AU - El-Haj, Mahmoud

AU - Rayson, Paul Edward

AU - Young, Steven Eric

AU - Walker, Martin

AU - Moore, Andrew

AU - Athanasakou, Vasiliki

AU - Schleicher, Thomas

PY - 2016/5/23

Y1 - 2016/5/23

N2 - Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds ofattributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale.Our best machine learning algorithm correctly classified performance sentences with 70% accuracy and detected tone and attribution in financial PEAs with accuracy of 79%.

AB - Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds ofattributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale.Our best machine learning algorithm correctly classified performance sentences with 70% accuracy and detected tone and attribution in financial PEAs with accuracy of 79%.

KW - NLP

KW - Machine Learning

KW - Financial narratives

KW - financial documents

KW - PEA

KW - Text Analysis

KW - Natural Language Processing

UR - http://lrec2016.lrec-conf.org/en/

M3 - Conference contribution/Paper

SN - 9782951740891

SP - 1820

EP - 1825

BT - Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation

A2 - Calzolari, Nicoletta

A2 - Choukri, Khalid

A2 - Declerck, Thierry

A2 - Grobelnik, Marko

A2 - Maegaard, Bente

A2 - Mariani, Joseph

A2 - Moreno, Asuncion

A2 - Odijk, Jan

A2 - Piperidis, Stelios

PB - European Language Resources Association (ELRA)

ER -