Sentiment analysis and the impact of employee satisfaction on firm earnings

  • 4 Citations

Abstract

Prior text mining studies of corporate reputational sentiment based on newswires, blogs and Twitter feeds have mostly captured reputation from the perspective of two groups of stakeholders – the media and consumers. In this study we examine the sentiment of a potentially overlooked stakeholder group, namely, the firm’s employees. First, we present a novel dataset that uses online employee reviews to capture employee satisfaction. We employ LDA to identify salient aspects in employees’ reviews, and manually infer one latent topic that appears to be associated with the firm’s outlook. Second, we create a composite document by aggregating employee reviews for each firm and measure employee sentiment as the polarity of the composite document using the General Inquirer dictionary to count positive and negative terms. Finally, we define employee satisfaction as a weighted combination of the firm outlook topic cluster and employee sentiment. The results of our joint aspect-polarity model suggest that it may be beneficial for investors to incorporate a measure of employee satisfaction into their method for forecasting firm earnings.
Original languageUndefined
Title of host publication36th European Conference on IR Research, ECIR 2014
Place of PublicationLondon
PublisherSpringer Verlag
Pages519-527
Number of pages9
ISBN (Print)978-3-319-06027-9
DOIs
StatePublished - Apr 2014
Event36th European Conference on Information Retrieval, ECIR 2014 - Amsterdam, Netherlands

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume8416
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference36th European Conference on Information Retrieval, ECIR 2014
Abbreviated titleECIR
CountryNetherlands
CityAmsterdam
Period13/04/1416/04/14

Fingerprint

Employees
Sentiment
Stakeholders

Keywords

  • EWI-24670
  • Text Mining
  • Social Media
  • Sentiment Analysis
  • IR-91062
  • frim reputation
  • Finance
  • METIS-305867
  • employee satisfaction

Cite this

Moniz, A., & de Jong, F. M. G. (2014). Sentiment analysis and the impact of employee satisfaction on firm earnings. In 36th European Conference on IR Research, ECIR 2014 (pp. 519-527). (Lecture Notes in Computer Science; Vol. 8416). London: Springer Verlag. DOI: 10.1007/978-3-319-06028-6_51

Moniz, Andy; de Jong, Franciska M.G. / Sentiment analysis and the impact of employee satisfaction on firm earnings.

36th European Conference on IR Research, ECIR 2014. London : Springer Verlag, 2014. p. 519-527 (Lecture Notes in Computer Science; Vol. 8416).

Research output: Scientific - peer-reviewConference contribution

@inbook{130f3732969d47f7ab4cdb41bb46af73,
title = "Sentiment analysis and the impact of employee satisfaction on firm earnings",
abstract = "Prior text mining studies of corporate reputational sentiment based on newswires, blogs and Twitter feeds have mostly captured reputation from the perspective of two groups of stakeholders – the media and consumers. In this study we examine the sentiment of a potentially overlooked stakeholder group, namely, the firm’s employees. First, we present a novel dataset that uses online employee reviews to capture employee satisfaction. We employ LDA to identify salient aspects in employees’ reviews, and manually infer one latent topic that appears to be associated with the firm’s outlook. Second, we create a composite document by aggregating employee reviews for each firm and measure employee sentiment as the polarity of the composite document using the General Inquirer dictionary to count positive and negative terms. Finally, we define employee satisfaction as a weighted combination of the firm outlook topic cluster and employee sentiment. The results of our joint aspect-polarity model suggest that it may be beneficial for investors to incorporate a measure of employee satisfaction into their method for forecasting firm earnings.",
keywords = "EWI-24670, Text Mining, Social Media, Sentiment Analysis, IR-91062, frim reputation, Finance, METIS-305867, employee satisfaction",
author = "Andy Moniz and {de Jong}, {Franciska M.G.}",
year = "2014",
month = "4",
doi = "10.1007/978-3-319-06028-6_51",
isbn = "978-3-319-06027-9",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "519--527",
booktitle = "36th European Conference on IR Research, ECIR 2014",

}

Moniz, A & de Jong, FMG 2014, Sentiment analysis and the impact of employee satisfaction on firm earnings. in 36th European Conference on IR Research, ECIR 2014. Lecture Notes in Computer Science, vol. 8416, Springer Verlag, London, pp. 519-527, 36th European Conference on Information Retrieval, ECIR 2014, Amsterdam, Netherlands, 13-16 April. DOI: 10.1007/978-3-319-06028-6_51

Sentiment analysis and the impact of employee satisfaction on firm earnings. / Moniz, Andy; de Jong, Franciska M.G.

36th European Conference on IR Research, ECIR 2014. London : Springer Verlag, 2014. p. 519-527 (Lecture Notes in Computer Science; Vol. 8416).

Research output: Scientific - peer-reviewConference contribution

TY - CHAP

T1 - Sentiment analysis and the impact of employee satisfaction on firm earnings

AU - Moniz,Andy

AU - de Jong,Franciska M.G.

PY - 2014/4

Y1 - 2014/4

N2 - Prior text mining studies of corporate reputational sentiment based on newswires, blogs and Twitter feeds have mostly captured reputation from the perspective of two groups of stakeholders – the media and consumers. In this study we examine the sentiment of a potentially overlooked stakeholder group, namely, the firm’s employees. First, we present a novel dataset that uses online employee reviews to capture employee satisfaction. We employ LDA to identify salient aspects in employees’ reviews, and manually infer one latent topic that appears to be associated with the firm’s outlook. Second, we create a composite document by aggregating employee reviews for each firm and measure employee sentiment as the polarity of the composite document using the General Inquirer dictionary to count positive and negative terms. Finally, we define employee satisfaction as a weighted combination of the firm outlook topic cluster and employee sentiment. The results of our joint aspect-polarity model suggest that it may be beneficial for investors to incorporate a measure of employee satisfaction into their method for forecasting firm earnings.

AB - Prior text mining studies of corporate reputational sentiment based on newswires, blogs and Twitter feeds have mostly captured reputation from the perspective of two groups of stakeholders – the media and consumers. In this study we examine the sentiment of a potentially overlooked stakeholder group, namely, the firm’s employees. First, we present a novel dataset that uses online employee reviews to capture employee satisfaction. We employ LDA to identify salient aspects in employees’ reviews, and manually infer one latent topic that appears to be associated with the firm’s outlook. Second, we create a composite document by aggregating employee reviews for each firm and measure employee sentiment as the polarity of the composite document using the General Inquirer dictionary to count positive and negative terms. Finally, we define employee satisfaction as a weighted combination of the firm outlook topic cluster and employee sentiment. The results of our joint aspect-polarity model suggest that it may be beneficial for investors to incorporate a measure of employee satisfaction into their method for forecasting firm earnings.

KW - EWI-24670

KW - Text Mining

KW - Social Media

KW - Sentiment Analysis

KW - IR-91062

KW - frim reputation

KW - Finance

KW - METIS-305867

KW - employee satisfaction

U2 - 10.1007/978-3-319-06028-6_51

DO - 10.1007/978-3-319-06028-6_51

M3 - Conference contribution

SN - 978-3-319-06027-9

T3 - Lecture Notes in Computer Science

SP - 519

EP - 527

BT - 36th European Conference on IR Research, ECIR 2014

PB - Springer Verlag

ER -

Moniz A, de Jong FMG. Sentiment analysis and the impact of employee satisfaction on firm earnings. In 36th European Conference on IR Research, ECIR 2014. London: Springer Verlag. 2014. p. 519-527. (Lecture Notes in Computer Science). Available from, DOI: 10.1007/978-3-319-06028-6_51