Insights into Employer Attractiveness through Text Analytics: Evidence from the Financial Technology Industry

Text analytics refers to deriving meaningful information from textual data. Text analytics involves using multiple techniques and tools to extract meaningful insights from various data sources like text documents, emails, online or offline feedback, conversations, or posts on various social media platforms. This study investigates the role of text analytics in understanding and enhancing employer attractiveness. Employer attractiveness can be understood or referred to as an employer’s ability to attract, engage, and retain talented employees. Reviews or feedback of employees from a prominent job portal are analyzed. Techniques like text preprocessing, sentiment analysis, topic modeling, word frequency analysis, and word cloud visualization are used to explore and understand employer attractiveness. Positive reviews and comments suggest that an organization’s culture, professional growth opportunities, and job satisfaction are the most important parameters for employees. Excessive workload, management practices, and work-life balance are major concerns of employees, which can be understood from the analysis of negative remarks. While this study is specific to the Fintech (Financial Technology) industry, it lays the groundwork for future research across various other sectors and countries.