Extracting collective trends from Twitter using social-based data mining
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2013Citation
10.1007/978-3-642-40495-5_62
Computational Collective Intelligence. Technologies and Applications: 5th International Conference, ICCCI 2013, Craiova, Romania, September 11-13, 2013, Proceedings. Lecture Notes in Computer Science, Volumen 8083. Springer, 2013. 622-630.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-642-40494-8 (print); 978-3-642-40495-5 (online)DOI
10.1007/978-3-642-40495-5_62Funded by
The preparation of this manuscript has been supported by the Spanish Ministry of Science and Innovation under the following projects: TIN2010-19872, ECO2011-30105 (National Plan for Research, Development and Innovation) and the Multidisciplinary Project of Universidad Aut´onoma de Madrid (CEMU-2012-034)Editor's Version
http://dx.doi.org/10.1007/978-3-642-40495-5_62Subjects
Classification; Clustering; Collective trends; Data mining; Social network; Twitter; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40495-5_62Proceedings 5th International Conference, ICCCI 2013, Craiova, Romania, September 11-13, 2013,
Rights
© Springer-Verlag Berlin Heidelberg 2011Abstract
Social Networks have become an important environment for Collective Trends extraction. The interactions amongst users provide information of their preferences and relationships. This information can be used to measure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of the most relevant and popular Social Network is Twitter. This Social Network was created to share comments and opinions. The information provided by users is specially useful in different fields and research areas such as marketing. This data is presented as short text strings containing different ideas expressed by real people. With this representation, different Data Mining and Text Mining techniques (such as classification and clustering) might be used for knowledge extraction trying to distinguish the meaning of the opinions. This work is focused on the analysis about how these techniques can interpret these opinions within the Social Network using information related to IKEA® company.
Files in this item
Google Scholar:Bello Orgaz, Gema
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Menéndez, Héctor D.
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Okazaki, Shintaro
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Camacho, David
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