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.