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Visualize LDA Topic Correlations

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This example shows how to analyze correlations between topics in a Latent Dirichlet Allocation (LDA) topic model.

A latent Dirichlet allocation (LDA) model is a topic model which discovers underlying topics in a collection of documents and infers word probabilities in topics. The vectors of per-topic word probabilities characterize the topics. Using the per-topic word probabilities, you can identify correlations between the topics.

Load LDA Model

Load the LDA model factoryReportsLDAModel which is trained using a data set of factory reports detailing different failure events. For an example showing how to fit an LDA model to a collection of text data, see Analyze Text Data Using Topic Models.

load factoryReportsLDAModel
mdl
mdl = 
  ldaModel with properties:

                     NumTopics: 7
             WordConcentration: 1
            TopicConcentration: 0.5755
      CorpusTopicProbabilities: [0.1587 0.1573 0.1551 0.1534 0.1340 0.1322 0.1093]
    DocumentTopicProbabilities: [480×7 double]
        TopicWordProbabilities: [158×7 double]
                    Vocabulary: ["item"    "occasionally"    "get"    "stuck"    "scanner"    "spool"    "loud"    "rattling"    "sound"    "come"    "assembler"    "piston"    "cut"    "power"    "start"    "plant"    "capacitor"    "mixer"    …    ]
                    TopicOrder: 'initial-fit-probability'
                       FitInfo: [1×1 struct]

Visualize the topics using word clouds.

numTopics = mdl.NumTopics;

figure
t = tiledlayout("flow");
title(t,"LDA Topics")

for i = 1:numTopics
    nexttile
    wordcloud(mdl,i);
    title("Topic " + i)
end

Figure contains objects of type wordcloud. The chart of type wordcloud has title Topic 1. The chart of type wordcloud has title Topic 2. The chart of type wordcloud has title Topic 3. The chart of type wordcloud has title Topic 4. The chart of type wordcloud has title Topic 5. The chart of type wordcloud has title Topic 6. The chart of type wordcloud has title Topic 7.

Visualize Topic Correlations

Calculate the correlations between the topics using the corrcoef function with the LDA model topic word probabilities as input.

correlation = corrcoef(mdl.TopicWordProbabilities);

View the correlations in a heat map and label each topic with its top three words. To prevent the heat map from highlighting the trivial correlations between topics each and itself, subtract the identity matrix from the correlations.

For each topic, find the top three words.

numTopics = mdl.NumTopics;
for i = 1:numTopics
    top = topkwords(mdl,3,i);
    topWords(i) = join(top.Word,", ");
end

Plot the correlations using the heatmap function.

figure
heatmap(correlation - eye(numTopics), ...
    XDisplayLabels=topWords, ...
    YDisplayLabels=topWords)

title("LDA Topic Correlations")
xlabel("Topic")
ylabel("Topic")

Figure contains an object of type heatmap. The chart of type heatmap has title LDA Topic Correlations.

For each topic, find the topic with the strongest correlation and display the pairs in a table with the corresponding correlation coefficient.

[topCorrelations,topCorrelatedTopics] = max(correlation - eye(numTopics));

tbl = table;
tbl.TopicIndex = (1:numTopics)';
tbl.Topic = topWords';
tbl.TopCorrelatedTopicIndex = topCorrelatedTopics';
tbl.TopCorrelatedTopic = topWords(topCorrelatedTopics)';
tbl.CorrelationCoefficient = topCorrelations'
tbl=7×5 table
    TopicIndex                Topic                 TopCorrelatedTopicIndex          TopCorrelatedTopic          CorrelationCoefficient
    __________    ______________________________    _______________________    ______________________________    ______________________

        1         "mixer, sound, assembler"                    5               "mixer, fuse, coolant"                    0.34304       
        2         "scanner, agent, stuck"                      4               "scanner, appear, spool"                  0.34526       
        3         "sound, agent, hear"                         1               "mixer, sound, assembler"                 0.26909       
        4         "scanner, appear, spool"                     2               "scanner, agent, stuck"                   0.34526       
        5         "mixer, fuse, coolant"                       1               "mixer, sound, assembler"                 0.34304       
        6         "arm, robot, smoke"                          1               "mixer, sound, assembler"               0.0042125       
        7         "software, sorter, controller"               7               "software, sorter, controller"                  0

See Also

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