Decision Support System for Corporate Reputation Based Social Media Listening Using a Cross-Source Sentiment Analysis Engine

R. Loke, S. Pathak

2023

Abstract

This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.

Paper Citation

in Harvard Style

Loke R. and Pathak S. (2023). Decision Support System for Corporate Reputation Based Social Media Listening Using a Cross-Source Sentiment Analysis Engine. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 559-567. DOI: 10.5220/0012136400003541

in Bibtex Style

@conference{data23,
author={R. Loke and S. Pathak},
title={Decision Support System for Corporate Reputation Based Social Media Listening Using a Cross-Source Sentiment Analysis Engine},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={559-567},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012136400003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Decision Support System for Corporate Reputation Based Social Media Listening Using a Cross-Source Sentiment Analysis Engine
SN - 978-989-758-664-4
AU - Loke R.
AU - Pathak S.
PY - 2023
SP - 559
EP - 567
DO - 10.5220/0012136400003541
PB - SciTePress