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Visualization of High-Level Associations from Twitter Data
Abstract
The Data Mining and Knowledge Discovery (KDD) process focuses on extracting useful information from large datasets. To support analysts in making decisions, a relevant research effort has been devoted to visualizing the extracted data mining models effectively. A particular attention has been paid to the discovery of strong association rules from textual data coming from social networks, which represent potentially relevant correlations among document terms. However, state-of-the-art rule visualization tools do not allow experts to visualize data correlations at different abstraction levels. Hence, the effectiveness of the proposed approaches is limited, especially when dealing with fairly sparse data. This chapter presents Twitter Generalized Rule Visualizer (TGRV), a novel text mining and visualization tool. It aims at supporting analysts in looking into the results of the generalized association rule mining process from textual data coming from Twitter supplied with WordNet taxonomies. Taxonomies are used for aggregating document terms into higher-level concepts. Generalized rules represent high-level associations among document terms. By exploiting taxonomy-based models, experts may look into the discovered data correlations from different perspectives and figure out interesting knowledge. Changing the perspective from which data correlations are visualized is shown to improve the readability and the usability of the generated rule-based model. The experimental results show the applicability and the usefulness of the proposed visualization tool on real textual data coming from Twitter. The visualized data correlations are shown to be valuable for advanced analysis, such as topic trend and user behavior analysis.
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