Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | 36th European Conference on IR Research, ECIR 2014 |
| Place of Publication | London |
| Publisher | Springer |
| Pages | 519-527 |
| Number of pages | 9 |
| ISBN (Print) | 978-3-319-06027-9 |
| DOIs | |
| Publication status | Published - Apr 2014 |
| Event | 36th European Conference on Information Retrieval, ECIR 2014: (IR Resarch) - Amsterdam, Netherlands Duration: 13 Apr 2014 → 16 Apr 2014 Conference number: 36 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer Verlag |
| Volume | 8416 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 36th European Conference on Information Retrieval, ECIR 2014 |
|---|---|
| Abbreviated title | ECIR |
| Country | Netherlands |
| City | Amsterdam |
| Period | 13/04/14 → 16/04/14 |
Keywords
- EWI-24670
- Text Mining
- Social Media
- Sentiment Analysis
- IR-91062
- frim reputation
- Finance
- METIS-305867
- employee satisfaction
Cite this
}
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, 2014. p. 519-527 (Lecture Notes in Computer Science; Vol. 8416).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
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
CY - London
ER -