Database Reference
In-Depth Information
Many of the techniques that we mentioned in the previous sections such as cluster-
ing and classification can be adapted to the text mining, with the proper representa-
tion of the text. We could use K-means clustering or other methods to tie the text into
meaningful groups of subjects. Sentiment analysis and spam filtering are examples
of a classification tasks in text mining (recall that we listed spam filtering as a prom-
inent use case for Naïve Bayesian classifier). In addition to the traditional statistical
methods, natural language processing methods are also used in this phase.
It should be noted that the list of tasks are not ordered. One generally starts with the
parsing, either with the intention of compiling them into a searchable corpus or cata-
log (maybe after some analytical tasks such as tagging or categorization), or spe-
cifically for the purpose of text mining. So it's not a process, it's a set of things that
go into the text analysis task. Or maybe a tree, where you start with parsing, and go
down to either search or to text mining
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