| E. Dritsas, M. Trigka, G. Vonitsanos, A. Kanavos, Ph. Mylonas |
| Aspect-Based Community Detection of Cultural Heritage Streaming Data |
| 12th International Conference on Information, Intelligence, Systems and Applications (IISA 2021), 12-15 July 2021, Chania, Crete, Greece |
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ABSTRACT
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| Twitter is one of the most popular online social networks providing a huge amount of data produced by users interactions through tweets. After an appropriate analysis of this data, groups of users who share similar attributes, emotions, opinions, and preferences can be identified. Massive cultural content management are important because reviews and opinions may be analyzed in order to extract meaningful representations. In this paper, an aspect mining method of a cultural heritage scenario by taking advantage of Apache Spark streaming architecture is presented. Specifically, we propose the combination of a community detection detection algorithm, i.e. the Parallel Structural Clustering Algorithm for Networks (PSCAN), with a topic modelling methods, i.e. the Latent Dirichlet Allocation (LDA), for performing large-scale data analysis in Twitter.
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| 15 July , 2021 |
| E. Dritsas, M. Trigka, G. Vonitsanos, A. Kanavos, Ph. Mylonas, "Aspect-Based Community Detection of Cultural Heritage Streaming Data", 12th International Conference on Information, Intelligence, Systems and Applications (IISA 2021), 12-15 July 2021, Chania, Crete, Greece |
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