Clustering Twitter Feeds using Word Co-occurrence CS769 Project Report

Clustering Twitter Feeds using Word Co-occurrence CS769 Project Report. Khot, T.

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.

@article{
 title = {Clustering Twitter Feeds using Word Co-occurrence CS769 Project Report},
 type = {article},
 websites = {http://pages.cs.wisc.edu/~tushar/projects/cs769.pdf},
 id = {2b9d7ecf-dda5-37a2-b999-d2a6f41aa3cc},
 created = {2018-02-05T17:30:24.237Z},
 accessed = {2018-02-05},
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 profile_id = {371589bb-c770-37ff-8193-93c6f25ffeb1},
 group_id = {f982cd63-7ceb-3aa2-ac7e-a953963d6716},
 last_modified = {2018-02-05T17:30:26.270Z},
 read = {false},
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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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