Automatic detection of potential customers by opinion mining and intelligent agents

• Autorzy: Moreno Raúl , Fernandez-Isabel Alberto , Martín De Diego Isaac , Moguerza Javier M. , Lancho Carmen , Cuesta Santa Teresa Marina

Identyfikatory

DOI

10.15439/2022F131

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Nowadays, customer acquisition is an open issue that has a special interest in all companies over the world. Very different marketing campaigns using psychological methodologies are designed to address this issue. However, once a campaign is launched, it is highly complicated to detect which sets of customers are most likely to purchase an offered product. This fact is a key objective since it allows companies to focus their efforts on specific clients and discard others. Several selection techniques have been implemented but most of them are usually very demanding in terms of time and human resources for the companies. Artificial Intelligence techniques appear to help simplifying the process. Thus, companies have started to use Machine Learning (ML) models trained to efficiently detect those clients with certain proneness to purchase. In this sense, this paper presents a novel purchase propensity detection ML system based on the Sentiment Analysis techniques able to consider the customer comments regarding the offered products. The tourist domain has been selected for the case study, where the obtained product was successfully embedded in an initial prototype.

Słowa kluczowe

EN

sentiment analysis; neural network; prototype; psychology; company; computer architecture; machine learning

PL

analiza nastrojów; sieć neuronowa; prototyp; psychologia; firma; uczenie maszynowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 93--101
• Opis fizyczny: Bibliogr. 31 poz., tab., rys.

Twórcy

autor

Moreno Raúl

• Madox Viajes Rey Juan Carlos University Arroyomolinos (Madrid), Spain

autor

Fernandez-Isabel Alberto

• Rey Juan Carlos University Data Science Laboratory Móstoles (Madrid), Spain

autor

Martín De Diego Isaac

• Rey Juan Carlos University Data Science Laboratory Móstoles (Madrid), Spain

autor

Moguerza Javier M.

• Rey Juan Carlos University Data Science Laboratory Móstoles (Madrid), Spain

autor

Lancho Carmen

• Rey Juan Carlos University Data Science Laboratory Móstoles (Madrid), Spain

autor

Cuesta Santa Teresa Marina

• Rey Juan Carlos University Data Science Laboratory Móstoles (Madrid), Spain

Bibliografia

• 1. J. S. Thomas, “A methodology for linking customer acquisition to customer retention,” Journal of marketing research, vol. 38, no. 2, pp. 262–268, 2001. http://dx.doi.org/10.1509/jmkr.38.2.262.18848
• 2. O. Mintz, I. S. Currim, and I. Jeliazkov, “Information processing pattern and propensity to buy: An investigation of online point-of-purchase behavior,” Marketing Science, vol. 32, no. 5, pp. 716–732, 2013. http://dx.doi.org/10.1287/mksc.2013.0790
• 3. K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: tasks, approaches and applications,” Knowledge-based systems, vol. 89, pp. 14–46, 2015. http://dx.doi.org/10.1016/j.knosys.2015.06.015
• 4. E. Cambria, Y. Li, F. Z. Xing, S. Poria, and K. Kwok, “Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis,” in Proceedings of the 29th ACM international conference on information & knowledge management, 2020. http://dx.doi.org/10.1145/3340531.3412003 pp. 105–114.
• 5. N. Medagoda, S. Shanmuganathan, and J. Whalley, “Sentiment lexicon construction using sentiwordnet 3.0,” in 2015 11th International Conference on Natural Computation (ICNC). IEEE, 2015. http://dx.doi.org/10.1109/ICNC.2015.7378094 pp. 802–807.
• 6. M. Ahmad, S. Aftab, S. S. Muhammad, and S. Ahmad, “Machine learning techniques for sentiment analysis: A review,” Int. J. Multidiscip. Sci. Eng, vol. 8, no. 3, p. 27, 2017. http://dx.doi.org/10.18090/samriddhi.v12i02.03
• 7. S. Bhattacharya, D. Sarkar, D. K. Kole, and P. Jana, “Recent trends in recommendation systems and sentiment analysis,” Advanced Data Mining Tools and Methods for Social Computing, pp. 163–175, 2022. http://dx.doi.org/10.1016/B978-0-32-385708-6.00016-3
• 8. R. Prabowo and M. Thelwall, “Sentiment analysis: A combined approach,” Journal of Informetrics, vol. 3, no. 2, pp. 143–157, 2009. http://dx.doi.org/10.1016/j.joi.2009.01.003
• 9. A. Onan, “Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks,” Concurrency and Computation: Practice and Experience, vol. 33, no. 23, p. e5909, 2021. http://dx.doi.org/10.1002/cpe.5909
• 10. M. Pota, M. Ventura, H. Fujita, and M. Esposito, “Multilingual evaluation of pre-processing for bert-based sentiment analysis of tweets,” Expert Systems with Applications, vol. 181, p. 115119, 2021. http://dx.doi.org/10.1016/j.eswa.2021.115119
• 11. N. Liu, B. Shen, Z. Zhang, Z. Zhang, and K. Mi, “Attention-based sentiment reasoner for aspect-based sentiment analysis,” Human-centric Computing and Information Sciences, vol. 9, no. 1, pp. 1–17, 2019. http://dx.doi.org/10.1186/s13673-019-0196-3
• 12. A. Onan, “Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach,” Computer Applications in Engineering Education, vol. 29, no. 3, pp. 572–589, 2021. http://dx.doi.org/10.1002/cae.22253
• 13. S. Wassan, X. Chen, T. Shen, M. Waqar, and N. Jhanjhi, “Amazon product sentiment analysis using machine learning techniques,” Revista Argentina de Clínica Psicológica, vol. 30, no. 1, p. 695, 2021. http://dx.doi.org/10.24205/03276716.2020.2065
• 14. P. K. Mallick, P. Dutta, S. Mishra, and M. K. Mishra, “Sentiment analysis and evaluation of movie reviews using classifiers,” in Cognitive Informatics and Soft Computing. Springer, 2021. http://dx.doi.org/10.1007/978-981-16-1056-1_5 pp. 53–59.
• 15. M. Uddin, Q. Wang, H. H. Wei, H. L. Chi, and M. Ni, “Building information modeling (bim), system dynamics (sd), and agentbased modeling (abm): Towards an integrated approach,” Ain Shams Engineering Journal, vol. 12, no. 4, pp. 4261–4274, 2021. http://dx.doi.org/10.1016/j.asej.2021.04.015
• 16. A. Garro, M. Mühlhäuser, A. Tundis, M. Baldoni, C. Baroglio, F. Bergenti, P. Torroni et al., “Intelligent agents: Multi-agent systems,” Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, vol. 315, 2018. http://dx.doi.org/10.1016/B978-0-12-809633-8.20328-2
• 17. A. Fernández-Isabel, R. Fuentes-Fernández, and I. M. de Diego, “Modeling multi-agent systems to simulate sensor-based smart roads,” Simulation Modelling Practice and Theory, vol. 99, p. 101994, 2020. http://dx.doi.org/10.1016/j.simpat.2019.101994
• 18. C. González-Fernández, J. Cabezas, A. Fernández-Isabel, and I. Martín de Diego, “Combining multi-agent systems and subjective logic to develop decision support systems,” in International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer, 2020. http://dx.doi.org/10.1007/978-3-030-50146-4_12 pp. 143–157.
• 19. J. Cabezas, A. Fernandez-Isabel, R. R. Fernández, C. González-Fernández, A. Alonso, and I. M. de Diego, “Bio-inspired agent-based architecture for fraud detection,” in Proceedings of the 2020 3rd International Conference on Information Management and Management Science, 2020. http://dx.doi.org/10.1145/3416028.3416039 pp. 67–71.
• 20. J. Wang, Y. Hong, J. Wang, J. Xu, Y. Tang, Q.-L. Han, and J. Kurths, “Cooperative and competitive multi-agent systems: From optimization to games,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 5, pp. 763–783, 2022. http://dx.doi.org/10.1109/JAS.2022.105506
• 21. P. Siswahyudi, T. A. Kurniawan, and V. Sugiarto, “Agent-oriented methodologies comparison: A literature review,” Advanced Science Letters, vol. 24, no. 11, pp. 8710–8716, 2018. http://dx.doi.org/10.1166/asl.2018.12331
• 22. F. Bergenti, G. Caire, S. Monica, and A. Poggi, “The first twenty years of agent-based software development with jade,” Autonomous Agents and Multi-Agent Systems, vol. 34, no. 2, pp. 1–19, 2020. http://dx.doi.org/10.1007/s10458-020-09460-z
• 23. J. Kazil, D. Masad, and A. Crooks, “Utilizing python for agent-based modeling: the mesa framework,” in International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Springer, 2020. http://dx.doi.org/10.1007/978-3-030-61255-9_30 pp. 308–317.
• 24. B. K. Kler, “Tourism and restoration,” in Philosophical issues in tourism. Channel View Publications, 2009. http://dx.doi.org/10.21832/9781845410988 pp. 117–134.
• 25. A. Vinerean, “Motivators that intervene in the decision making process in tourism,” Expert journal of marketing, vol. 2, no. 2, 2014.
• 26. J. Swarbrooke and S. Horner, Consumer behavior in tourism. Heinemann, Oxford, 2007.
• 27. S. Amaro and P. Duarte, “An integrative model of consumers’ intentions to purchase travel online,” Tourism management, vol. 46, pp. 64–79, 2015. http://dx.doi.org/10.1016/j.tourman.2014.06.006
• 28. R. P. Falcao, J. B. Ferreira, and M. Carrazedo Marques da Costa Filho, “The influence of ubiquitous connectivity, trust, personality and generational effects on mobile tourism purchases,” Information Technology & Tourism, vol. 21, no. 4, pp. 483–514, 2019. http://dx.doi.org/10.1007/s40558-019-00154-1
• 29. A. A. Mahrous and S. S. Hassan, “Achieving superior customer experience: An investigation of multichannel choices in the travel and tourism industry of an emerging market,” Journal of Travel Research, vol. 56, no. 8, pp. 1049–1064, 2017. http://dx.doi.org/10.1177/0047287516677166
• 30. L. De Silva, F. R. Meneguzzi, and B. Logan, “Bdi agent architectures: A survey,” in Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020, Japão., 2020. http://dx.doi.org/10.17863/CAM.53101
• 31. L. S. Melo, R. F. Sampaio, R. P. S. Leão, G. C. Barroso, and J. R. Bezerra, “Python-based multi-agent platform for application on power grids,” International transactions on electrical energy systems, vol. 29, no. 6, p. e12012, 2019. http://dx.doi.org/10.1002/2050-7038.12012

Uwagi

1. This research has been supported by the grants from Madrid Autonomous Community (Ref: IND2019/TIC-17194), the Spanish Ministry of Economy and Competitiveness, under the Retos-Investigación program: MODAS-IN (Ref: RTI-2018-094269-B-I00), and Rey Juan Carlos University (Ref: C1PREDOC2020); and donation of the Titan V GPU by NVIDIA Corporation.

2. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).