loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: R. E. Loke and O. Kachaniuk

Affiliation: Centre for Market Insights, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

Keyword(s): Natural Language Processing (NLP), Sentiment Analysis, Corporate Review Data, Supervised Learning, biLSTM Neural Network with Attention Mechanism, Word Embeddings.

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%.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.117.137.64

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - DATA; ISBN 978-989-758-440-4; ISSN 2184-285X, SciTePress, pages 310-317. DOI: 10.5220/0009892303100317

@conference{data20,
author={R. E. 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 - DATA},
year={2020},
pages={310-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009892303100317},
isbn={978-989-758-440-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - 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
IS - 2184-285X
AU - Loke, R.
AU - Kachaniuk, O.
PY - 2020
SP - 310
EP - 317
DO - 10.5220/0009892303100317
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic