Sentiment analysis and the impact of employee satisfaction on firm earnings

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    8 Citations (Scopus)
    241 Downloads (Pure)

    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
    Pages519-527
    Number of pages9
    ISBN (Print)978-3-319-06027-9
    DOIs
    Publication statusPublished - Apr 2014
    Event36th European Conference on Information Retrieval, ECIR 2014: (IR Resarch) - Amsterdam, Netherlands
    Duration: 13 Apr 201416 Apr 2014
    Conference number: 36

    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

    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. https://doi.org/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, 2014. pp. 519-527 (Lecture Notes in Computer Science).
    @inproceedings{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.}",
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    doi = "10.1007/978-3-319-06028-6_51",
    language = "Undefined",
    isbn = "978-3-319-06027-9",
    series = "Lecture Notes in Computer Science",
    publisher = "Springer",
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    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, London, pp. 519-527, 36th European Conference on Information Retrieval, ECIR 2014, Amsterdam, Netherlands, 13/04/14. https://doi.org/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, 2014. p. 519-527 (Lecture Notes in Computer Science; Vol. 8416).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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

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    AU - de Jong, Franciska M.G.

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    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.

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    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. 2014. p. 519-527. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-06028-6_51