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Interpreting Extracting Topics from Data (LDA)

FreeThoughtsFreeThoughts Member Posts: 1 Newbie
edited October 10 in Help
Hi im currently working with the LDA operator from the Operator took box after accomplishing extracting the data I wanted to properly interpret the data. Was wondering if you could help me my code is shown below. The issue I have understanding it what words fall under what specific topic without slowly having to analysis by hand. As well as the open visualization for each topic

<?xml version="1.0" encoding="UTF-8"?><process version="9.4.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.4.000" expanded="true" name="Process" origin="GENERATED_TUTORIAL">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.4.001" expanded="true" height="68" name="Retrieve Practice LDA" width="90" x="45" y="85">
        <parameter key="repository_entry" value="//Local Repository/data/Practice LDA"/>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="8.1.000" expanded="true" height="82" name="Process Documents from Data" width="90" x="246" y="85">
        <parameter key="create_word_vector" value="true"/>
        <parameter key="vector_creation" value="Binary Term Occurrences"/>
        <parameter key="add_meta_information" value="true"/>
        <parameter key="keep_text" value="true"/>
        <parameter key="prune_method" value="percentual"/>
        <parameter key="prune_below_percent" value="1.35"/>
        <parameter key="prune_above_percent" value="100.0"/>
        <parameter key="prune_below_rank" value="0.05"/>
        <parameter key="prune_above_rank" value="0.95"/>
        <parameter key="datamanagement" value="double_sparse_array"/>
        <parameter key="data_management" value="auto"/>
        <parameter key="select_attributes_and_weights" value="true"/>
        <list key="specify_weights">
          <parameter key="LinguisticSentence" value="1.0"/>
        </list>
        <process expanded="true">
          <operator activated="true" class="text:transform_cases" compatibility="8.2.000" expanded="true" height="68" name="Transform Cases (2)" width="90" x="45" y="34">
            <parameter key="transform_to" value="lower case"/>
          </operator>
          <operator activated="true" class="text:tokenize" compatibility="8.2.000" expanded="true" height="68" name="Tokenize (2)" width="90" x="179" y="34">
            <parameter key="mode" value="non letters"/>
            <parameter key="characters" value=".:"/>
            <parameter key="language" value="English"/>
            <parameter key="max_token_length" value="3"/>
          </operator>
          <operator activated="true" class="open_file" compatibility="9.4.001" expanded="true" height="68" name="Open File" width="90" x="313" y="187">
            <parameter key="resource_type" value="file"/>
            <parameter key="filename" value="C:\Users\Christian\Downloads\Stopwords.xlsx"/>
          </operator>
          <operator activated="true" class="text:filter_by_length" compatibility="8.2.000" expanded="true" height="68" name="Filter Tokens (by Length) (2)" width="90" x="313" y="34">
            <parameter key="min_chars" value="2"/>
            <parameter key="max_chars" value="100"/>
          </operator>
          <operator activated="true" class="text:filter_stopwords_dictionary" compatibility="8.2.000" expanded="true" height="82" name="Filter Stopwords (Dictionary) (2)" width="90" x="447" y="34">
            <parameter key="case_sensitive" value="false"/>
            <parameter key="encoding" value="SYSTEM"/>
          </operator>
          <operator activated="true" class="text:filter_stopwords_english" compatibility="8.2.000" expanded="true" height="68" name="Filter Stopwords (English)" width="90" x="581" y="34"/>
          <operator activated="true" class="text:stem_porter" compatibility="8.2.000" expanded="true" height="68" name="Stem (Porter)" width="90" x="715" y="34"/>
          <connect from_port="document" to_op="Transform Cases (2)" to_port="document"/>
          <connect from_op="Transform Cases (2)" from_port="document" to_op="Tokenize (2)" to_port="document"/>
          <connect from_op="Tokenize (2)" from_port="document" to_op="Filter Tokens (by Length) (2)" to_port="document"/>
          <connect from_op="Open File" from_port="file" to_op="Filter Stopwords (Dictionary) (2)" to_port="file"/>
          <connect from_op="Filter Tokens (by Length) (2)" from_port="document" to_op="Filter Stopwords (Dictionary) (2)" to_port="document"/>
          <connect from_op="Filter Stopwords (Dictionary) (2)" from_port="document" to_op="Filter Stopwords (English)" to_port="document"/>
          <connect from_op="Filter Stopwords (English)" from_port="document" to_op="Stem (Porter)" to_port="document"/>
          <connect from_op="Stem (Porter)" 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="operator_toolbox:lda_exampleset" compatibility="2.2.000" expanded="true" height="124" name="Extract Topics from Data (LDA)" width="90" x="514" y="85">
        <parameter key="text_attribute" value="text"/>
        <parameter key="number_of_topics" value="10"/>
        <parameter key="use_alpha_heuristics" value="true"/>
        <parameter key="alpha_sum" value="0.1"/>
        <parameter key="use_beta_heuristics" value="true"/>
        <parameter key="beta" value="0.01"/>
        <parameter key="optimize_hyperparameters" value="true"/>
        <parameter key="optimize_interval_for_hyperparameters" value="10"/>
        <parameter key="top_words_per_topic" value="5"/>
        <parameter key="iterations" value="1000"/>
        <parameter key="reproducible" value="false"/>
        <parameter key="enable_logging" value="false"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <connect from_op="Retrieve Practice LDA" 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="Extract Topics from Data (LDA)" to_port="exa"/>
      <connect from_op="Extract Topics from Data (LDA)" from_port="exa" 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>


Tghadially

Answers

  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,256   Unicorn
    @mschmitz is the resident guru for LDA and he can help provide some guidance with this I think

    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
    Tghadiallysgenzer
  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 2,156  RM Data Scientist
    Hi @FreeThoughts
    the operator provides an example set with the most important words per topic - isn't this what you need?

    BR,
    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
    FreeThoughts
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