Clustering Twitter Feeds using Word Co-occurrence CS769 Project Report. Khot, T.
Clustering Twitter Feeds using Word Co-occurrence CS769 Project Report [pdf]Paper  Clustering Twitter Feeds using Word Co-occurrence CS769 Project Report [pdf]Website  abstract   bibtex   
For very large number of documents, normal clustering meth-ods would take O(document 2) time. When the number of documents are very large but short such as tweets, it may make sense to actually cluster the words. We present a method that clusters the words using the word co-occurrence as a similarity measure. We use spectral clustering for cre-ating word clusters and do a " search " to get the actual doc-uments. The resulting word clusters and tweets make sense most of the times.
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 title = {Clustering Twitter Feeds using Word Co-occurrence CS769 Project Report},
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 websites = {http://pages.cs.wisc.edu/~tushar/projects/cs769.pdf},
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 created = {2018-02-05T17:30:24.237Z},
 accessed = {2018-02-05},
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 abstract = {For very large number of documents, normal clustering meth-ods would take O(document 2) time. When the number of documents are very large but short such as tweets, it may make sense to actually cluster the words. We present a method that clusters the words using the word co-occurrence as a similarity measure. We use spectral clustering for cre-ating word clusters and do a " search " to get the actual doc-uments. The resulting word clusters and tweets make sense most of the times.},
 bibtype = {article},
 author = {Khot, Tushar}
}
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