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Paperwork often inundates human resources departments, suggesting value in analytics that identify the most pressing issues of employee dissatisfaction. This study assesses text analytics techniques for this problem by utilizing Indeed. com employee reviews. These techniques provide value to human resources management by rapidly prioritizing documents. We compare general sentiment analyses with dictionaries curated for this domain, and the domain-specific approaches offer superior prediction of employee dissatisfaction. These results demonstrate the capability of text analytics in human resources management and offer tools for professionals to prioritize documents in need of the most urgent review.