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. A previous study showed the possibility of early detection of network problems by monitoring Twitter. However, since Twitter includes many conversation topics, it is difficult to find tweets that relate to network problems. Searching by a particular keyword is insufficient since it produces a lot of false positive
specific keywords concerns. At the same time, according to specific keywords related to micro-Bo, we can calculate the emotions of related microblogging via Ren-CECps. Finally, according to changes from time to time, we make a corresponding data charts, visual display of such trends. In this study, we illustrate that we can
Clustering of keywords in tweets is studied. A series of tweets is handled as a sequence of words and an inner product space is introduced to a set of keywords on the basis of positive definite kernels using a fuzzy neighborhood defined on that sequence. Methods of agglomerative hierarchical clustering as well as c
2) a qualitative analysis. The performance of the model with text input is better than the performance of the model without the text input, which has been a typical approach of applying the Gaussian process. Moreover, the model can capture keywords which provide behind rational of the prediction result, which was not
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