Optimizing Natural Language Processing Applications for Sentiment Analysis

Anderson Lopes, Vitoria Gomes, Geraldo Zafalon

2024

Abstract

Recent technological advances have stimulated the exponential growth of social network data, driving an increase in research into sentiment analysis. Thus, studies exploring the intersection of Natural Language Processing and social network analysis are playing an important role, specially those one focused on heuristic approaches and the integration of algorithms with machine learning. This work centers on the application of sentiment analysis techniques, employing algorithms such as Logistic Regression and Support Vector Machines. The analyses were performed on datasets comprising 5,000 and 10,000 tweets, and our findings reveal the efficient performance of Logistic Regression in comparison with other approach. Logistc Regression improved the performed in almost all measures, with emphasis to accuracy, recall and F1-Score.

Paper Citation

in Harvard Style

Lopes A., Gomes V. and Zafalon G. (2024). Optimizing Natural Language Processing Applications for Sentiment Analysis. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 698-705. DOI: 10.5220/0012632000003690

in Bibtex Style

@conference{iceis24,
author={Anderson Lopes and Vitoria Gomes and Geraldo Zafalon},
title={Optimizing Natural Language Processing Applications for Sentiment Analysis},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={698-705},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012632000003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Optimizing Natural Language Processing Applications for Sentiment Analysis
SN - 978-989-758-692-7
AU - Lopes A.
AU - Gomes V.
AU - Zafalon G.
PY - 2024
SP - 698
EP - 705
DO - 10.5220/0012632000003690
PB - SciTePress