Abstract
New mapping and location service applications have focused on offering improved usability and service based on multimodal passenger experiences from door to door. This helps citizens to develop greater confidence in and adherence to multimodal transport services. These applications focus on adapting to the needs of users during their journeys thanks to the data, statistics and trends provided by the passenger experiences while using these platforms. The My-Trac application is dedicated to the research and development of these user-centred services to improve the multimodal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks such as twitter. In this article we present a system that allows the creation of a profile of preferences of a certain user based on the tweets published in his Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns them as a result in a document containing the categories and the degree of affinity that the user maintains with each one. In this way the user can be offered activities or services during the route to be taken with a high degree of affinity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Java, A., Song, X., Finin, T., Tseng, B.: Why we Twitter: under- standing microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an in UENCER: quantifying in UENCE on Twitter. In: Proceedings of the Fourth ACM international Conference on Web Search and Data Mining, pp. 65–74 (2011)
Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics (2012)
Cotelo, J.M., Cruz, F.L., Enríquez, F., Troyano, J.A.: Tweet categorization by combining content and structural knowledge. Inf. Fus. 31, 54–64 (2016)
Lee, K., Palsetia, D., Narayanan, Patwary, Md.M.A., Agrawal, A., Choudhary, A.: Twitter trending topic classification. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 251–258. IEEE (2011)
Yale University. About yale: Yale facts. https://www.yale.edu/about-yale/yale-facts (2017)
Evgenia, A., Vassilis, A., Konstantinos, D., Kosmides, P.: An offline, statistical method for cost efficient design of experiments and field trials involving electric vehicles. In: Proceedings of the 11th ITS European Congress, 06-09 June 2016, Glasgow, Scotland (2016)
Acknowledgments
This research has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777640).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cea-Morán, J.J., González-Briones, A., De La Prieta, F., Prat-Pérez, A., Prieto, J. (2021). Extraction of Travellers’ Preferences Using Their Tweets. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications . ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-58356-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58355-2
Online ISBN: 978-3-030-58356-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)