Sentiment Analysis and the Impact of Employee Satisfaction on Firm Earnings

• Published in: Advances in Information Retrieval pp. 519 - 527
• Main Authors: Moniz, Andy, de Jong, Franciska
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Earning Announcement; Earning Surprise; Employee Satisfaction; Latent Dirichlet Allocation; Sentiment Analysis
• ISBN: 9783319060279; 3319060279
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-06028-6_51

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.