Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining

Bajpai, R and Hazarika, D and Singh, K and Gorantla, S and Cambria, E and Zimmermann, R (2023) Aspect-Sentiment Embeddings for Company Profiling and Employee Opinion Mining. In: 19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018, 18-24 March 2018, Hanoi, pp. 142-160.

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Abstract

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. © 2023, Springer Nature Switzerland AG.

Item Type:	Conference Paper
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords:	Embeddings; Machine learning; Personnel, Aspect-based sentiment analyse; Company profiling; Embeddings; Ensemble approaches; Extreme learning machine; Learning machines; Opinion mining; Sentiment analysis; Sentiment embedding, Sentiment analysis
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	29 Mar 2023 10:47
Last Modified:	29 Mar 2023 10:47
URI:	https://eprints.iisc.ac.in/id/eprint/81175

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