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Extract-Transform-Load Process for Recognizing Sentiment from User-Generated Text on Social Media

Topics: Analysis and Design Methods; Big Data for SSE; Business Process Management and Engineering; Enterprise Systems Modelling and Architecture ; Meta Programming Systems and Meta Modeling; SSE for Data and Process Mining ; SSE for Social Computing ; User-Centered Software Engineering

Authors: Afef Walha 1 ; 2 ; Faiza Ghozzi 1 ; 3 and Faiez Gargouri 1 ; 3

Affiliations: 1 MIRACL Laboratory, Sfax, Tunisia ; 2 Higher Institute of Information Science and Multimedia of Gabes (ISIMG), University of Gabes, Tunisia ; 3 Higher Institute of Information Science and Multimedia of Sfax (ISIMS), University of Sfax, Tunisia

Keyword(s): Sentiment, Classification, BPMN, Polarity, ETL, Process, Formalization, Social Media.

Abstract: In today’s world, business intelligence systems must incorporate opinion mining into their decision-making process. Sentiment analysis of user-generated content on social media has gained significant attention in recent years. This method collects user opinions, feelings, and attitudes toward a topic of interest and helps determine whether their sentiment is positive, neutral, or negative. This paper addresses text classification in sentiment analysis and presents a solution to the Extract-Transform-Load (ETL) process based on a lexicon approach. This process involves gathering media clips, converting them into sentiments, and loading them into a social data warehouse. We provide generic and customizable models to aid designers in integrating pre-processing techniques and sentiment analysis into the ETL process. By formalizing new ETL concepts, designers can create a reliable conceptual design for any ETL process related to opinion data integration from social media.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Walha, A.; Ghozzi, F. and Gargouri, F. (2024). Extract-Transform-Load Process for Recognizing Sentiment from User-Generated Text on Social Media. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-696-5; ISSN 2184-4895, SciTePress, pages 641-648. DOI: 10.5220/0012706100003687

@conference{enase24,
author={Afef Walha. and Faiza Ghozzi. and Faiez Gargouri.},
title={Extract-Transform-Load Process for Recognizing Sentiment from User-Generated Text on Social Media},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2024},
pages={641-648},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012706100003687},
isbn={978-989-758-696-5},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Extract-Transform-Load Process for Recognizing Sentiment from User-Generated Text on Social Media
SN - 978-989-758-696-5
IS - 2184-4895
AU - Walha, A.
AU - Ghozzi, F.
AU - Gargouri, F.
PY - 2024
SP - 641
EP - 648
DO - 10.5220/0012706100003687
PB - SciTePress