Combining Social-Based Data Mining Techniques To Extract Collective Trends From Twitter

Malaysian Journal of Computer Science

David Camacho¹  Gema Bello-Orgaz¹  Shintaro Okazaki¹  Héctor Menéndez¹ 

关键词: Collective Trends;    Social Network;    Classification;    Clustering;    Twitter;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: University of Malaya * Faculty of Computer Science and Information Technology

【 摘 要 】

Social Networks have become an important environment for Collective Trends extraction. The interactionsamongst users provide information of their preferences and relationships. This information can be used tomeasure the influence of ideas, or opinions, and how they are spread within the Network. Currently, one of themost relevant and popular Social Networks is Twitter. This Social Network was created to share comments andopinions. The information provided by users is especially useful in different fields and research areas such asmarketing. This data is presented as short text strings containing different ideas expressed by real people. Withthis representation, different Data Mining techniques (such as classification or clustering) will be used forknowledge extraction to distinguish the meaning of the opinions. Complex Network techniques are also helpfulto discover influential actors and study the information propagation inside the Social Network. This work isfocused on how clustering and classification techniques can be combined to extract collective knowledge fromTwitter. 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 toimprove the classification results. Finally, these results are compared against a dataset which has beenmanually labelled by human experts to analyse the accuracy of the proposed method.

【 授权许可】

Unknown