Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining
This addresses the need for better company profiling and employee opinion mining, though it is incremental as it applies existing sentiment analysis techniques to a new domain.
The paper tackles the problem of ranking companies by developing a generalized metric based on employee opinions, using aspect-sentiment embeddings from Glassdoor reviews and achieving insights for customized selection.
With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task. Although there are many available metrics that rank companies, there is an inherent need for a generalized metric that takes into account the different aspects that constitute employee opinions of the companies. In this work, we aim to overcome the aforementioned problem by generating aspect-sentiment based embedding for the companies by looking into reliable employee reviews of them. We created a comprehensive dataset of company reviews from the famous website Glassdoor.com and employed a novel ensemble approach to perform aspect-level sentiment analysis. Although a relevant amount of work has been done on reviews centered on subjects like movies, music, etc., this work is the first of its kind. We also provide several insights from the collated embeddings, thus helping users gain a better understanding of their options as well as select companies using customized preferences.