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great attentions. In this paper, we advocate the significance of keyword co-occurrence for geo-tagged microblogs analysis, which has been overlooked by existing studies. The co-occurrence of keywords is necessary to resolve the ambiguity in event analysis, especially when different events have overlapping descriptions
sense information in the real world and mention them online. The web interfaces of these networks often support features such as keyword search that allow an user to quickly find entities of interest. While these interfaces are adequate for regular users, they are often too restrictive to answer complex queries such as (1
Can keyword-hashtag networks, derived from Big Data environments such as Twitter, yield clinicians a powerful tool to extrapolate patterns that may lead to development of new medical therapy and/or drugs? In our paper, we present a systematic network mining method to answer this question. We present HashnetMiner, a
media data sets: data is large, noisy, and dynamic. To collect the data related to a specific topic and keyword efficiently, we propose a new algorithm that selects the best seed nodes with limited resources and time. The algorithm also evaluates various user influence and activity factors, and updates the seed nodes
There is no previous research that compares the results of k-means, CLOPE clustering and Latent Dirichlet Allocation (LDA) topic modeling algorithms for detecting trending topics on tweets. Since not all tweets contain hashtags, we considered three training data feature sets: hashtags, keywords and keywords + hashtags
, irrelevant tweets were further segregated by means of a unigram dictionary containing education-oriented keywords. The Apriori algorithm was then applied to the dataset thus obtained resulting in characteristic markers or patterns of these institutes.
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