Text Topic Modeling with LDA operator

go09cp

I am trying to figure out how the LDA operator works. I have a data file on excel with 630 rows of text data. I  ran this through Process Documents from Data to generate the 'term occurrence' word vector as stated in the LDA manual. However when I feed this into the LDA operator, after a while I simply get an out-of-memory error. I've tried it with 4GB of RAM and 16 GB of RAM. What am I doing wrong? Attached is the xml. Thank you.

<?xml version="1.0" encoding="UTF-8"?><process version="7.5.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.5.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="read_excel" compatibility="7.5.001" expanded="true" height="68" name="Read Excel" width="90" x="112" y="85">
        <parameter key="excel_file" value="C:\Users\Pari\OneDrive\ADMIN onedrive\Projects\Valbot\Rel8ed\Valbot RapidMiner-Export-Import\Data\Landscape Text from Blogs.xlsx"/>
        <parameter key="imported_cell_range" value="A1:A637"/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Name"/>
        </list>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="backyard landscaping ideas oakville ontario backyard landscaping ideas oakville ontario backyard la….true.text.attribute"/>
        </list>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="7.5.000" expanded="true" height="82" name="Process Documents from Data" width="90" x="246" y="85">
        <parameter key="vector_creation" value="Term Occurrences"/>
        <parameter key="prune_method" value="absolute"/>
        <parameter key="prune_below_absolute" value="50"/>
        <parameter key="prune_above_absolute" value="600"/>
        <list key="specify_weights"/>
        <process expanded="true">
          <operator activated="true" class="text:transform_cases" compatibility="7.5.000" expanded="true" height="68" name="Transform Cases" width="90" x="45" y="34"/>
          <operator activated="true" class="text:tokenize" compatibility="7.5.000" expanded="true" height="68" name="Tokenize" width="90" x="179" y="34"/>
          <operator activated="true" class="text:filter_stopwords_english" compatibility="7.5.000" expanded="true" height="68" name="Filter Stopwords (English)" width="90" x="313" y="34"/>
          <operator activated="true" class="text:filter_by_length" compatibility="7.5.000" expanded="true" height="68" name="Filter Tokens (by Length)" width="90" x="447" y="34">
            <parameter key="min_chars" value="3"/>
          </operator>
          <connect from_port="document" to_op="Transform Cases" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_op="Filter Stopwords (English)" to_port="document"/>
          <connect from_op="Filter Stopwords (English)" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
          <connect from_op="Filter Tokens (by Length)" 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="corpus_linguistics_plugin_LDA:lda_topic_model" compatibility="1.1.001" expanded="true" height="145" name="Latent Dirichlet Allocation" width="90" x="447" y="85"/>
      <connect from_op="Read Excel" from_port="output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" to_op="Latent Dirichlet Allocation" to_port="example set of documents as Bag-of-Words vectors with term occurrences"/>
      <connect from_op="Latent Dirichlet Allocation" from_port="cluster 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"/>
    </process>
  </operator>
</process>

 

Telcontar120

I'm not sure which extension you are using in your attached process.  The LDA topic modeler operator that RapidMiner recently released is in the Operator Toolbox extension.  That operator requires you to convert your text to a document first (which you can do from your Excel file).  That should then run ok.