1 research outputs found
Combining social-based data mining techniques to extract collective trends from twitter
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 Networks is Twitter. This Social Network was created to share comments and
opinions. The information provided by users is especially 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 techniques (such as classification or clustering) will be used for
knowledge extraction to distinguish the meaning of the opinions. Complex Network techniques are also helpful
to discover influential actors and study the information propagation inside the Social Network. This work is
focused on how clustering and classification techniques can be combined to extract collective knowledge from
Twitter. In an initial phase, clustering techniques are applied to extract the main topics from the user opinions.
Later, the collective knowledge extracted is used to relabel the dataset according to the clusters obtained to
improve the classification results. Finally, these results are compared against a dataset which has been
manually labelled by human experts to analyse the accuracy of the proposed method.The preparation of this manuscript has been supported by the Spanish Ministry of Science and Innovation under the
following projects: TIN2010-19872 and ECO2011-30105 (National Plan for Research, Development and
Innovation), as well as the Multidisciplinary Project of Universidad Autónoma de Madrid (CEMU2012-034). The
authors thank Ana M. DÃaz-MartÃn and Mercedes Rozano for the manual classification of the Tweets