Sentiment Analysis in Analyzing Monkeypox-Related Tweets Based on Deep Learning

Yidan Wang

2024

Abstract

As the internet and social media platforms have rapidly expanded, sentiment analysis has emerged as a significant branch of natural language processing, focused on understanding individuals' emotions and attitudes toward specific topics. This article provides a comprehensive review of sentiment analysis evolution, from early dictionary-based methods to modern deep learning techniques. The focus is on a comparative evaluation of model outcomes from particular research, underscoring the effective performance of a combined Convolutional Neural Networks- Long Short-Term Memory (CNN-LSTM) deep learning model in analyzing sentiment within Monkeypox-related tweets. This model harnesses the local feature recognition of CNNs and the sequential data processing of LSTMs for accurate sentiment detection. Extensive experiments have demonstrated that this model outperforms standalone CNN-LSTM models in terms of stability and generalization capabilities. Future research will focus on utilizing more sophisticated sentiment analysis techniques, such as hierarchical attention networks, and cross-domain models, to enhance precision and applicability in various practical applications.

Paper Citation

in Harvard Style

Wang Y. (2024). Sentiment Analysis in Analyzing Monkeypox-Related Tweets Based on Deep Learning. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 215-220. DOI: 10.5220/0012923200004508

in Bibtex Style

@conference{emiti24,
author={Yidan Wang},
title={Sentiment Analysis in Analyzing Monkeypox-Related Tweets Based on Deep Learning},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={215-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012923200004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Sentiment Analysis in Analyzing Monkeypox-Related Tweets Based on Deep Learning
SN - 978-989-758-713-9
AU - Wang Y.
PY - 2024
SP - 215
EP - 220
DO - 10.5220/0012923200004508
PB - SciTePress