Wiki-LDA: A Mixed-Method Approach for Effective Interest Mining on Twitter Data
| Type of publication: | Conference paper |
| Citation: | Pu_INTERNATIONALCONFERENCEONCOMPUTERSUPPORTEDEDUCATION_2016 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of CSEDU 2016 |
| Year: | 2016 |
| Month: | April |
| Abstract: | Learning analytics (LA) and Educational data mining (EDM) have emerged as promising technology-enhanced learning (TEL) research areas in recent years. Both areas deal with the development of methods that harness educational data sets to support the learning process. A key area of application for LA and EDM is learner modelling. Learner modelling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. As learning is increasingly happening in open and distributed environments beyond the classroom and access to information in these environments is mostly interest-driven, learner interests need to constitute an important learner feature to be modeled. In this paper, we focus on the interest dimension of a learner model and present Wiki-LDA as a novel method to effectively mine user’s interests in Twitter. We apply a mixed-method approach that combines Latent Dirichlet Allocation (LDA), text mining APIs, and wikipedia categories. Wiki-LDA has proven effective at the task of interest mining and classification on Twitter data, outperforming standard LDA. |
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| Projects |
Idiap MODERN |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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