ETL Transformation Algorithm for Facebook Opinion Data

Afef Walha, Faiza Ghozzi, Faiez Gargouri


Considered as a rich source of information, social networking sites have been created lot of buzz because people share and discuss their opinions freely. Sentiment analysis is used for knowing voice or response of crowd for products, services, organizations, individuals, events, etc. Due to their importance, people opinions are analyzed in several domains including information retrieval, semantic web, text mining. These researches define new classification techniques to assign positive or negative opinion. Decisional systems like WeBhouse, known by their data-consuming must be enriched by this kind of pertinent opinions to give better help to decision makers. Nevertheless, cleaning and transformation processes recognized by several approaches as a key of WeBhouse development, don’t deal with sentiment analysis. To fulfill this gap, we propose a new analysis algorithm which determines user’s sentiment score of a post shared on the social network Facebook. This algorithm analyzes user’s opinion depending on opinion terms and emoticons included in his comments. This algorithm is integrated in transformation process of ETL approach.


  1. Abbasi, A., Chen, H., Salem, A., 2008. Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums. In ACM Transactions on Information Systems Journal.
  2. Akkaoui, Z., E., Mazón, J., Vaisman, A. A., Zimányi, E., 2012. BPMN-Based Conceptual Modeling of ETL Processes. In DAWAK'12, 14th International Conference on Data Warehousing and Knowledge Discovery, pages 1-14, Springer.
  3. API, 2015. API Graph Explorer Tool, “https://”.
  4. El-Sappagh, S., H., Hendawi, A., M., Bastawissy, A., H., 2011. A proposed model for data warehouse ETL processes. In Journal of King Saud University - Computer and Information Sciences, pages 91-104, Elsevier.
  5. Hogenboom, A., Bal, D., Frasincar, F., 2013. Exploiting Emoticons in Sentiment Analysis. In SAC'13, 28th Annual ACM Symposium on Applied Computing, pages 703-710.
  6. Hu, M., Liu, B., 2004. Mining and summarizing customer reviews. In KDD'04, international conference on Knowledge Discovery and Data Mining, pages 168- 177, ACM.
  7. Hu, Y., Li, W., 2011. Document Sentiment Classification by Exploring Description Model of Topical Terms. In Computer Speech Language Journal, pages 386-403, Elsevier.
  8. Jiao, J., Zhou, Y., 2011. Sentiment Polarity Analysis based Multi Dictionary. In ICPST'11, International Conference on Physics Science and Technology, Elsevier.
  9. Kim, S., Hovy, E., 2004. Determining the Sentiment of Opinions. In COLING'04, 20th International conference on Computational Linguistics.
  10. Liu, B., 2011. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer-Verlag Berlin Heidelberg, 2nd Edition.
  11. Medhat, W., Hassan, A., Korashy, H., 2014. Sentiment Analysis Algorithms and Applications: A Survey. In Ain Shams Engineering Journal, pages 1093-1113.
  12. Muñoz, L., Mazón, J.N., Trujillo, J., 2010. A Family of Experiments to Validate Measures for UML Activity Diagrams of ETL Processes in Data Warehouse. In Information & Software Technology, pages 1188- 1203, Elsevier.
  13. Qiu, G., He, X., Zhang. F., Shi, Y., Bu, J., Chen, C., 2010. DASA: Dissatisfaction-Oriented Advertising Based on Sentiment Analysis. In Expert Systems with Application Journal, pages 6182-6191, Elsevier.
  14. Trujillo, J., Luján-Mora, S., 2003. A UML Based Approach For Modeling ETL Processes in Data Warehouses. In ER'03, 22nd International Conference on Conceptual Modeling, pages 307-320, Springer.
  15. Vashisht, S., Thakur, S., 2014. Facebook as a Corpus for Emoticons-Based Sentiment Analysis. In IJETAE'14, International Journal of Emerging Technology and Advanced Engineering, pages 904-908.
  16. Vassiliadis, P., 2009. A Survey of Extract-TransformLoad Technology. In IJDWM'09, International Journal of Data Warehousing & Mining, pages 1-27.
  17. Walha, A., Ghozzi, F., Gargouri, F., 2015. ETL design toward social network opinion analysis. In SERA'15, 13th IEEE/ACIS on Software Engineering, Reasearch, Management and applications, Springer (to appear).
  18. Wilkinson, K., Simitsis, A., Dayal, U., Castellanos, M., 2010. Leveraging Business Process Models for ETL Design. In ER'10, 29th International Conference on Conceptual Modeling, Springer.
  19. Wilson, T., Wiebe, J., Hoffmann, P., 2005. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In HLT'05, 2005 Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 347-354.

Paper Citation

in Harvard Style

Walha A., Ghozzi F. and Gargouri F. (2015). ETL Transformation Algorithm for Facebook Opinion Data . In Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-106-9, pages 150-155. DOI: 10.5220/0005494101500155

in Bibtex Style

author={Afef Walha and Faiza Ghozzi and Faiez Gargouri},
title={ETL Transformation Algorithm for Facebook Opinion Data},
booktitle={Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - ETL Transformation Algorithm for Facebook Opinion Data
SN - 978-989-758-106-9
AU - Walha A.
AU - Ghozzi F.
AU - Gargouri F.
PY - 2015
SP - 150
EP - 155
DO - 10.5220/0005494101500155