Collaborative company profiling: Insights from an employee's perspective

Abstract

Company profiling is an analytical process to build an indepth understanding of company's fundamental characteristics. It serves as an effective way to gain vital information of the target company and acquire business intelligence. Traditional approaches for company profiling rely heavily on the availability of rich finance information about the company, such as finance reports and SEC filings, which may not be readily available for many private companies. However, the rapid prevalence of online employment services enables a new paradigm-to obtain the variety of company's information from their employees' online ratings and comments. This, in turn, raises the challenge to develop company profiles from an employee's perspective. To this end, in this paper, we propose a method named Company Profiling based Collaborative Topic Regression (CPCTR), for learning the latent structural patterns of companies. By formulating a joint optimization framework, CPCTR has the ability in collaboratively modeling both textual (e.g., reviews) and numerical information (e.g., salaries and ratings). Indeed, with the identified patterns, including the positive/negative opinions and the latent variable that influences salary, we can effectively carry out opinion analysis and salary prediction. Extensive experiments were conducted on a real-world data set to validate the effectiveness of CPCTR. The results show that our method provides a comprehensive understanding of company characteristics and delivers a more effective prediction of salaries than other baselines.