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Publication Detail
Extracting collective trends from Twitter using social-based data mining
  • Publication Type:
  • Authors:
    Bello G, Menéndez H, Okazaki S, Camacho D
  • Publication date:
  • Pagination:
    622, 630
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    8083 LNAI
  • ISBN-13:
  • Status:
  • Print ISSN:
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 R can interpret these opinions within the Social Network using information related to IKEA® company. © Springer-Verlag 2013.
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