Aspect Based Sentiment Analysis on Online Review Data to Predict Corporate Reputation

R. Loke, W. Reitter

2021

Abstract

Corporate reputation is an intangible resource that is closely tied to an organization’s success but measuring it and to derive actions that can improve the reputations can be a long and expensive journey for an organization. In the available literature, corporate reputation is primarily measured through surveys, which can be time and cost intensive. This paper uses online reviews on the web as the source for a machine-learning driven aspect-based sentiment analysis that can enable organizations to evaluate their corporate reputation on a fine-grained level. The analysis is done unsupervised without organizations needing to manually label datasets. Using the insights generated through the analysis, on one hand, organizations can save costs and time to measure corporate reputation, and, on the other hand, it provides an in-depth analysis that splits the overall reputation into multiple aspects, with which organizations can identify weaknesses and in turn improve their corporate reputation. Therefore, this research is relevant for organizations aiming to understand and improve their corporate reputation to achieve success, for example, in form of financial performance, or for organizations that help and consult other organizations on their journeys to increased success. Our approach is validated, evaluated and illustrated with Trustpilot review data.

Paper Citation

in Harvard Style

Loke R. and Reitter W. (2021). Aspect Based Sentiment Analysis on Online Review Data to Predict Corporate Reputation. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-521-0, pages 343-352. DOI: 10.5220/0010607203430352

in Bibtex Style

@conference{data21,
author={R. Loke and W. Reitter},
title={Aspect Based Sentiment Analysis on Online Review Data to Predict Corporate Reputation},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2021},
pages={343-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010607203430352},
isbn={978-989-758-521-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Aspect Based Sentiment Analysis on Online Review Data to Predict Corporate Reputation
SN - 978-989-758-521-0
AU - Loke R.
AU - Reitter W.
PY - 2021
SP - 343
EP - 352
DO - 10.5220/0010607203430352