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• W4318261484 abstract "We present a literature survey of several concepts related to the problems addressed in this book. To address the problems of information filtering and organization, some frequently used techniques are filtering out unwanted information (e.g., spam), clustering, and summarization. Attribute selection and dimensionality reduction are important steps before clustering and summarization. The increasing and varied nature of microblog vocabulary enhances the role of attribute selection in data analysis. Attribute selection not only reduces the dimension of huge datasets, it also improves the production of summarization, clustering, and classification processes. The state-of-the-art attribute selection methods are discussed. Cluster analysis looks for patterns in datasets to identify similarity among the objects without any prior knowledge (i.e., unsupervised). Clustering is important especially for microblog data analysis. In this chapter, various standard clustering algorithms are briefly discussed. Some related topics like evaluation measures for cluster quality and cluster validation are also discussed. In the field of information retrieval, automatic document summarization is a well-known issue. During an emergency, hundreds to thousands of microblogs (tweets) are typically posted on Twitter, thus personally reading through each tweet is not practicable. As a result, summarizing microblogs made during emergency situations has become a popular research topic in recent years. Some off-the-shelf extractive summarization algorithms are discussed. Apart from discussing attribute selection, summarization, and clustering methods in general, this chapter also reviews several works that have applied these methods in the domain of online social microblogging sites." @default.
• W4318261484 created "2023-01-28" @default.
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• W4318261484 date "2023-01-01" @default.
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• W4318261484 title "Literature review on data analytics for social microblogging platforms" @default.
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• W4318261484 doi "https://doi.org/10.1016/b978-0-32-391785-8.00013-5" @default.
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