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Interpreting Extracting Topics from Data (LDA)

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>

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All comments

Telcontar120

@mschmitz is the resident guru for LDA and he can help provide some guidance with this I think

MartinLiebig

Hi @FreeThoughts
the operator provides an example set with the most important words per topic - isn't this what you need?

BR,
Martin

die_eike

I would like to revive this thread with more specific questions. Dear @mschmitz, it would be great if you could help me out with your knowledge.

I applied the LDA operator, the results are

(top): a list of topics with the top-ranked words per topic and their weights

(exa): a matrix with the documents I fed to the LDA as rows and the topics from (top) as columns. Each document is assigned a value (confidence) for each topic. One column shows the prediction to which topic each document belongs.

I have grasped from LDA that there is a distribution of words in a topic and a distribution of topics in a document. But how do we come to the values?

1. The weight in (top), how is this calculated? In my case, the same word occurs in different topics with different weights.

2. The confidence values in (exa) for "assignment" to a topic, how are these computed and how do I interpret them? Ok, the higher, the better. But is there a minimum threshold after which I can confidently state that this document belongs to that topic?

Looking forward to enlightment on these matters

MartinLiebig

Hi @die_eike ,

i think i need to push you a bit on a research travel.

What we do, is we embedd the Mallet library. So for

#2: Its Gibbs Sampling. The code we embedd is available here: https://github.com/mimno/Mallet/blob/master/src/cc/mallet/topics/TopicInferencer.java

for

#1: i think it is just how often they appeared. But i would need to check. The method used is: http://mallet.cs.umass.edu/api/cc/mallet/topics/ParallelTopicModel.html#getSortedWords()

Best,

Martin

die_eike

Dear @mschmitz,

thank you for your replay, there is really a lot to learn on this topic. If anybody is interested, I can recommend this blog entry, which provides an intuitive understanding of LDA and Gibbs sampling:

https://medium.com/analytics-vidhya/topic-modeling-using-lda-and-gibbs-sampling-explained-49d49b3d1045

Best,

Eike

Wine

Dear mschmitz,

I have the same question as die_eike. I saw all the metrics for the LDA through the links that were provided through the operator except for one, which is the weight of each word that falls under each topic. Been trying to look for what the word weight means and how it is computed. I wonder if you already have an idea? Thank you very much.

Wine

MartinLiebig

Hi @Wine ,

its sadly not that easy. As pointed out in the original paper: https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf

"The top words from some ofthe resulting multinomial distributionsp(wjz)are illustrated in Figure 8 (top)"

=> its part of the whole baysian optimization.

Best,

Martin

Wine

Thanks so much, Martin.