Sentiment Polarity Classification of Corporate Review Data with a Bidirectional Long-Short Term Memory (biLSTM) Neural Network Architecture

R. Loke, O. Kachaniuk

2020

Abstract

A considerable amount of literature has been published on Corporate Reputation, Branding and Brand Image. These studies are extensive and focus particularly on questionnaires and statistical analysis. Although extensive research has been carried out, no single study was found which attempted to predict corporate reputation performance based on data collected from media sources. To perform this task, a biLSTM Neural Network extended with attention mechanism was utilized. The advantages of this architecture are that it obtains excellent performance for NLP tasks. The state-of-the-art designed model achieves highly competitive results, F1 scores around 72%, accuracy of 92% and loss around 20%.

Paper Citation

in Harvard Style

Loke R. and Kachaniuk O. (2020). Sentiment Polarity Classification of Corporate Review Data with a Bidirectional Long-Short Term Memory (biLSTM) Neural Network Architecture.In Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-440-4, pages 310-317. DOI: 10.5220/0009892303100317

in Bibtex Style

@conference{data20,
author={R. Loke and O. Kachaniuk},
title={Sentiment Polarity Classification of Corporate Review Data with a Bidirectional Long-Short Term Memory (biLSTM) Neural Network Architecture},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2020},
pages={310-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009892303100317},
isbn={978-989-758-440-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Sentiment Polarity Classification of Corporate Review Data with a Bidirectional Long-Short Term Memory (biLSTM) Neural Network Architecture
SN - 978-989-758-440-4
AU - Loke R.
AU - Kachaniuk O.
PY - 2020
SP - 310
EP - 317
DO - 10.5220/0009892303100317