This example illustrates mining data that contains one column of text data. For an overview of text mining, see Text Mining.
To view the data, go to the schema for the data mining user account. Locate the table MINING_BUILD_TEXT
in Data Sources and double-click. Note that MINING_BUILD_TEXT
is listed among tables.
MINING_BUILD_TEXT
is essentially MINING_DATA_BUILD_V
converted to a table with a new column COMMENTS
consisting of customer comments.
We want to predict customers for whom the value of AFFINITY_CARD
is 1.
Follow these steps to build a classification model:
Classification
as the Function Type (this is the default), and select Support Vector Machine
as the Algorithm.MINING_BUILD_TEXT
resides as Schema, MINING_BUILD_TEXT
as the Table/View, and CUST_ID
as the Unique Identifier. For all other choices, use the defaults.AFFINITY_CARD
as the Target. Change the Mining Type of COMMENTS
to text
: Select the COMMENTS
row, click in the Mining Type column for COMMENTS
and select text
from the dropdown menu.
Note: You must change the mining type from categorical
to text
for text mining to take place. If the dropdown menu does not appear, you have selected an algorithm that does not support text mining.
DEMO_TEXT
.DEMO_TEXT
is displayed. Note that the activity has all of the steps of an activity that builds a Support Vector Machine model plus the steps Text and Test(Text). In these additional steps, Oracle Data Miner does all of the processing required to prepare the text column for mining. The Options for these steps support advanced text mining features such as customized stoplists.MINING_DATA_BUILD_V
, which does not contain a COMMENTS
column, the Predictive Confidence is approximately 60%.Copyright © 2008, Oracle. All rights reserved.