LSTM Network Learning for Sentiment Analysis

Badiâa Dellal-Hedjazi, Zaia Alimazighi

2022

Abstract

The strong economic issues (e-reputation, buzz detection ...) and political ( opinion leaders identification ...) explain the rapid rise of scientists on the topic of sentiment classification. Sentiment analysis focuses on the orientation of an opinion on an entity or its aspects. It determines its polarity which can be positive, neutral, or negative. Sentiment analysis is associated with texts classification problems. Deep Learning (machine learning technique) is based on multi-layer artificial neural networks. This technology has allowed scientists to make significant progress in data recognition and classification. What makes deep learning different from traditional machine learning methods is that during complex analyses, the basic features of the treatment will no longer be identified by human treatment in a previous algorithm, but directly by the deep learning. In this article we propose a Twitter sentiment analysis application using a deep learning algorithm with LSTM units adapted for natural language processing.

Paper Citation

in Harvard Style

Dellal-Hedjazi B. and Alimazighi Z. (2022). LSTM Network Learning for Sentiment Analysis. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-569-2, pages 449-454. DOI: 10.5220/0010964800003179

in Bibtex Style

@conference{iceis22,
author={Badiâa Dellal-Hedjazi and Zaia Alimazighi},
title={LSTM Network Learning for Sentiment Analysis},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2022},
pages={449-454},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010964800003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - LSTM Network Learning for Sentiment Analysis
SN - 978-989-758-569-2
AU - Dellal-Hedjazi B.
AU - Alimazighi Z.
PY - 2022
SP - 449
EP - 454
DO - 10.5220/0010964800003179