Performing Principal Component Analysis of a set of tweets.

rcamacho

Hello! First and foremost, I apologize if this topic has been found somewhere. I have spent a considerable amount of time attempting to look for a method.

 

I have found 2 social science studies that utilized PCA of text data using Rapid Miner. They displayed in a table which words had the highest eigenvalue for a particular factors. I am interested in learning how to do this, but thus far I have been frustrated with a lack of process/steps. I also wonder if it is something so elementary that there are no methods that explain the process?

 

To be more specific, I am interested in analyzing an excel file containing 2000 tweets (for starters). Thank you in advance for your sincere assistance!

Thomas_Ott

Well without reading the whole thing, it's kind of hard to figure out what they did exactly.

 

I suspect it must be something like this:

 

<?xml version="1.0" encoding="UTF-8"?><process version="7.2.003">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.2.003" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="social_media:search_twitter" compatibility="7.2.000" expanded="true" height="68" name="Search Twitter" width="90" x="112" y="34">
        <parameter key="connection" value="Twitter Connection"/>
        <parameter key="query" value="iphone"/>
        <parameter key="language" value="en"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="7.2.003" expanded="true" height="82" name="Select Attributes" width="90" x="246" y="34">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="Text"/>
      </operator>
      <operator activated="true" class="generate_attributes" compatibility="7.2.003" expanded="true" height="82" name="Generate Attributes" width="90" x="380" y="34">
        <list key="function_descriptions">
          <parameter key="label" value="&quot;iPhone&quot;"/>
        </list>
      </operator>
      <operator activated="true" class="social_media:search_twitter" compatibility="7.2.000" expanded="true" height="68" name="Search Twitter (2)" width="90" x="112" y="136">
        <parameter key="connection" value="Twitter Connection"/>
        <parameter key="query" value="samsung"/>
        <parameter key="language" value="en"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="7.2.003" expanded="true" height="82" name="Select Attributes (2)" width="90" x="246" y="136">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="Text"/>
      </operator>
      <operator activated="true" class="generate_attributes" compatibility="7.2.003" expanded="true" height="82" name="Generate Attributes (2)" width="90" x="380" y="136">
        <list key="function_descriptions">
          <parameter key="label" value="&quot;samsung&quot;"/>
        </list>
      </operator>
      <operator activated="true" class="append" compatibility="7.2.003" expanded="true" height="103" name="Append" width="90" x="581" y="34"/>
      <operator activated="true" class="set_role" compatibility="7.2.003" expanded="true" height="82" name="Set Role" width="90" x="715" y="34">
        <parameter key="attribute_name" value="label"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="nominal_to_text" compatibility="7.2.003" expanded="true" height="82" name="Nominal to Text" width="90" x="849" y="34">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="Text"/>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="7.2.001" expanded="true" height="82" name="Process Documents from Data" width="90" x="983" y="34">
        <parameter key="prune_method" value="percentual"/>
        <list key="specify_weights"/>
        <process expanded="true">
          <operator activated="true" class="text:tokenize" compatibility="7.2.001" expanded="true" height="68" name="Tokenize" width="90" x="179" y="34"/>
          <connect from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="principal_component_analysis" compatibility="7.2.003" expanded="true" height="103" name="PCA" width="90" x="1117" y="34"/>
      <connect from_op="Search Twitter" from_port="output" to_op="Select Attributes" to_port="example set input"/>
      <connect from_op="Select Attributes" from_port="example set output" to_op="Generate Attributes" to_port="example set input"/>
      <connect from_op="Generate Attributes" from_port="example set output" to_op="Append" to_port="example set 1"/>
      <connect from_op="Search Twitter (2)" from_port="output" to_op="Select Attributes (2)" to_port="example set input"/>
      <connect from_op="Select Attributes (2)" from_port="example set output" to_op="Generate Attributes (2)" to_port="example set input"/>
      <connect from_op="Generate Attributes (2)" from_port="example set output" to_op="Append" to_port="example set 2"/>
      <connect from_op="Append" from_port="merged set" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Nominal to Text" to_port="example set input"/>
      <connect from_op="Nominal to Text" from_port="example set output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" to_op="PCA" to_port="example set input"/>
      <connect from_op="PCA" from_port="example set output" to_port="result 2"/>
      <connect from_op="PCA" from_port="preprocessing model" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>

 

That said, I'm a bit cautious about the 100% accuracy of their model.