Abstract
With the ever-increasing number of social media messages posted daily, millions of users express opinions on various subjects, including opinions concerning the characteristics of products and services that they have already bought or they intend to buy in the near future. Accurately knowing the opinions of such a large number of users in near real time would be invaluable for the companies marketing those products. Thus, in the present paper, we propose an approach based on Semantic Web technologies, natural language processing and machine learning for accurately analysing the social media messages posted on Twitter. Compared to existing approaches, which mainly focus on determining the opinion of the user concerning the entire product, the approach proposed in the present paper offers deeper insights, by taking into consideration the fact that a user might have different and sometimes even contradictory opinions concerning the various characteristics of a single product. We start by creating an ontology for representing the relationships between the products and their characteristics, ontology that is also used for performing named entity recognition, given the fact that various users can employ different terms for referring to the same concept. The ontology is afterwards used in order to filter from the huge number of tweets published every minute only the ones that can prove relevant for the analysis. In the next step, aspect-based sentiment analysis is employed in order to determine the sentiment expressed by the social media user regarding one or several characteristics of the analysed product. The results of the analysis are stored as semantically structured data, thus making it possible to fully exploit the possibilities offered by Semantic Web technologies, such as inference and accessing the vast knowledge in Linked Open Data, for further analysis.
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Change history
04 April 2023
This book was inadvertently published with an incorrect spelling of the author’s name in Chapter 8 as Livu-Adrian Cotfas whereas it should be Liviu-Adrian Cotfas. In addition to this, the name of the author in Chapter 9 should be Liviu-Adrian Cotfas instead of Livu-Adrian Cotfas.
References
Allemang, D., & Hendler, J. (2011). Good and bad modeling practices. In D. Allemang & J. Hendler (Eds.), Semantic web for the working ontologist (2nd ed., pp. 307–324). Boston: Morgan Kaufmann.
Aloufi, S., & Saddik, A. E. (2018). Sentiment identification in football-specific tweets. IEEE Access, 6, 78609–78621.
Bao, Y., Quan, C., Wang, L., & Ren, F. (2014). The role of pre-processing in twitter sentiment analysis. In Intelligent computing methodologies (pp. 615–624). New York: Springer.
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59.
Ben Abbès, S., Scheuermann, A., Meilender, T., & D’Aquin, M. (2012). Characterizing modular ontologies. In 7th International Conference on Formal Ontologies in Information Systems – FOIS 2012 (pp. 13–25). [Online] Graz. Accessed April 14, 2019, from https://hal.archives-ouvertes.fr/hal-00710035
Borst, W. N. (1997). Construction of engineering ontologies for knowledge sharing and reuse. Universiteit Twente.
Cambria, E., Poria, S., Hazarika, D., & Kwok, K. (2018). SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) (pp. 1795–1802).
Cotfas, L.-A., Delcea, C., & Roxin, I. (2016a). Grey sentiment analysis using multiple lexicons. In Proceedings of the 15th International Conference on Conference on Informatics in Economy (IE 2016) (pp. 428–433). Cluj-Napoca: Bucharest University of Economic Studies Press.
Cotfas, L.-A., Delcea, C., Segault, A., & Roxin, I. (2016b). Semantic web-based social media analysis. In N. T. Nguyen & R. Kowalczyk (Eds.), Transactions on computational collective intelligence XXII (pp. 147–166). Berlin: Springer.
Cotfas, L.-A., Roxin, I., & Delcea, C. (2019). Semantic search in social media analysis. In Proceedings of the 18th International Conference on Conference on Informatics in Economy (IE 2019). [Online]. Education, Research and Business Technologies (pp. 37–42). Accessed September 4, 2019, from http://www.conferenceie.ase.ro/wp-content/uploads/2019/06/ProceedingsIE2019/semantic_search_in_social_media_analysis.pdf
Derczynski, L., Maynard, D., Rizzo, G., van Erp, M., Gorrell, G., Troncy, R., Petrak, J., & Bontcheva, K. (2015). Analysis of named entity recognition and linking for tweets. Information Processing & Management, 51(2), 32–49.
Dragoni, M., Poria, S., & Cambria, E. (2018). OntoSenticNet: A commonsense ontology for sentiment analysis. IEEE Intelligent Systems, 33(3), 77–85.
Dublin Core Metadata Initiative. (2018). DCMI: DCMI metadata terms. [Online] Accessed November 16, 2018, from http://dublincore.org/documents/dcmi-terms/
Ghobehei, M., Sadeghvaziri, F., Ebrahimi, E., & Afshar Bakeshloo, K. (2019). The effects of perceived brand orientation and perceived service quality in the higher education sector. Eurasian Business Review, 9(3), 347–365.
Kontopoulos, E., Berberidis, C., Dergiades, T., & Bassiliades, N. (2013). Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications, 40(10), 4065–4074.
Miles, A., & Bechhofer, S. (2009). SKOS Simple Knowledge Organization System. [online] Accessed November 16, 2018, from https://www.w3.org/TR/skos-reference/
Moslehpour, M., Wong, W.-K., Lin, Y. H., & Le Huyen Nguyen, T. (2018). Top purchase intention priorities of Vietnamese low cost carrier passengers: Expectations and satisfaction. Eurasian Business Review, 8(4), 371–389.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 2825–2830. Accessed September 10, 2019, from http://www.jmlr.org/papers/v12/pedregosa11a
RDF Working Group. (2014). RDF – Semantic Web Standards. [Online] Accessed November 18, 2018, from https://www.w3.org/RDF/
Twitter. (2019). Docs – Twitter developers. [Online] Twitter Developers. Accessed August 3, 2019, from https://developer.twitter.com/en/docs
W3C OWL Working Group. (2012). OWL 2 web ontology language document overview (2nd ed.). [Online] Accessed November 18, 2018, from https://www.w3.org/TR/owl2-overview/
Acknowledgements
This work was supported by a grant by UEFISCDI (“Unitatea Executivă pentru Finanţarea Învăţământului Superior, a Cercetării, Dezvoltării şi Inovării”), project FutureWeb (“Modelarea empirică şi dezvoltarea experimentală a instrumentelor asociate tehnologiilor emergente din domeniul reţelelor sociale online”), project number: PN-III-P1-1.2-PCCDI-2017-0800, 86PCCDI/2018.
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Cotfas, LA., Delcea, C., Nica, I. (2020). Analysing Customers’ Opinions Towards Product Characteristics Using Social Media. In: Bilgin, M.H., Danis, H., Demir, E., Tony-Okeke, U. (eds) Eurasian Business Perspectives. Eurasian Studies in Business and Economics, vol 15/2. Springer, Cham. https://doi.org/10.1007/978-3-030-48505-4_9
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