Analyze Text Data Using Topic Models

This example shows how to use the Latent Dirichlet Allocation (LDA) topic model to analyze text data.

A Latent Dirichlet Allocation (LDA) model is a topic model which discovers underlying topics in a collection of documents and infers the word probabilities in topics.

To reproduce the results of this example, set rng to 'default'.

rng('default')

Load and Extract Text Data

Load the example data. The file weatherReports.csv contains weather reports, including a text description and categorical labels for each event.

data = readtable("weatherReports.csv",'TextType','string');
head(data)

ans=8×16 table
            Time             event_id          state              event_type         damage_property    damage_crops    begin_lat    begin_lon    end_lat    end_lon                                                                                             event_narrative                                                                                             storm_duration    begin_day    end_day    year       end_timestamp    
    ____________________    __________    ________________    ___________________    _______________    ____________    _________    _________    _______    _______    _________________________________________________________________________________________________________________________________________________________________________________________________    ______________    _________    _______    ____    ____________________

    22-Jul-2016 16:10:00    6.4433e+05    "MISSISSIPPI"       "Thunderstorm Wind"       ""                "0.00K"         34.14        -88.63     34.122     -88.626    "Large tree down between Plantersville and Nettleton."                                                                                                                                                  00:05:00          22          22       2016    22-Jul-0016 16:15:00
    15-Jul-2016 17:15:00    6.5182e+05    "SOUTH CAROLINA"    "Heavy Rain"              "2.00K"           "0.00K"         34.94        -81.03      34.94      -81.03    "One to two feet of deep standing water developed on a street on the Winthrop University campus after more than an inch of rain fell in less than an hour. One vehicle was stalled in the water."       00:00:00          15          15       2016    15-Jul-0016 17:15:00
    15-Jul-2016 17:25:00    6.5183e+05    "SOUTH CAROLINA"    "Thunderstorm Wind"       "0.00K"           "0.00K"         35.01        -80.93      35.01      -80.93    "NWS Columbia relayed a report of trees blown down along Tom Hall St."                                                                                                                                  00:00:00          15          15       2016    15-Jul-0016 17:25:00
    16-Jul-2016 12:46:00    6.5183e+05    "NORTH CAROLINA"    "Thunderstorm Wind"       "0.00K"           "0.00K"         35.64        -82.14      35.64      -82.14    "Media reported two trees blown down along I-40 in the Old Fort area."                                                                                                                                  00:00:00          16          16       2016    16-Jul-0016 12:46:00
    15-Jul-2016 14:28:00    6.4332e+05    "MISSOURI"          "Hail"                    ""                ""              36.45        -89.97      36.45      -89.97    ""                                                                                                                                                                                                      00:07:00          15          15       2016    15-Jul-0016 14:35:00
    15-Jul-2016 16:31:00    6.4332e+05    "ARKANSAS"          "Thunderstorm Wind"       ""                "0.00K"         35.85         -90.1     35.838     -90.087    "A few tree limbs greater than 6 inches down on HWY 18 in Roseland."                                                                                                                                    00:09:00          15          15       2016    15-Jul-0016 16:40:00
    15-Jul-2016 16:03:00    6.4343e+05    "TENNESSEE"         "Thunderstorm Wind"       "20.00K"          "0.00K"        35.056       -89.937      35.05     -89.904    "Awning blown off a building on Lamar Avenue. Multiple trees down near the intersection of Winchester and Perkins."                                                                                     00:07:00          15          15       2016    15-Jul-0016 16:10:00
    15-Jul-2016 17:27:00    6.4344e+05    "TENNESSEE"         "Hail"                    ""                ""             35.385        -89.78     35.385      -89.78    "Quarter size hail near Rosemark."                                                                                                                                                                      00:05:00          15          15       2016    15-Jul-0016 17:32:00

Extract the text data from the field event_narrative.

textData = data.event_narrative;
textData(1:10)

ans = 10×1 string array
    "Large tree down between Plantersville and Nettleton."
    "One to two feet of deep standing water developed on a street on the Winthrop University campus after more than an inch of rain fell in less than an hour. One vehicle was stalled in the water."
    "NWS Columbia relayed a report of trees blown down along Tom Hall St."
    "Media reported two trees blown down along I-40 in the Old Fort area."
    ""
    "A few tree limbs greater than 6 inches down on HWY 18 in Roseland."
    "Awning blown off a building on Lamar Avenue. Multiple trees down near the intersection of Winchester and Perkins."
    "Quarter size hail near Rosemark."
    "Tin roof ripped off house on Old Memphis Road near Billings Drive. Several large trees down in the area."
    "Powerlines down at Walnut Grove and Cherry Lane roads."

Prepare Text Data for Analysis

Create a function which tokenizes and preprocesses the text data so it can be used for analysis. The function preprocessText, listed at the end of the example, performs the following steps in order:

1. Tokenize the text using tokenizedDocument.

2. Lemmatize the words using normalizeWords.

3. Erase punctuation using erasePunctuation.

4. Remove a list of stop words (such as "and", "of", and "the") using removeStopWords.

5. Remove words with 2 or fewer characters using removeShortWords.

6. Remove words with 15 or more characters using removeLongWords.

Use the preprocessing function preprocessText to prepare the text data.

documents = preprocessText(textData);
documents(1:5)

ans = 
  5×1 tokenizedDocument:

     5 tokens: large tree down plantersville nettleton
    18 tokens: two foot deep standing water develop street winthrop university campus inch rain fall less hour vehicle stall water
     9 tokens: nws columbia relay report tree blow down tom hall
    10 tokens: medium report two tree blow down i40 old fort area
     0 tokens:

Create a bag-of-words model from the tokenized documents.

bag = bagOfWords(documents)

bag = 
  bagOfWords with properties:

          Counts: [36176×18469 double]
      Vocabulary: [1×18469 string]
        NumWords: 18469
    NumDocuments: 36176

Remove words from the bag-of-words model that have do not appear more than two times in total. Remove any documents containing no words from the bag-of-words model.

bag = removeInfrequentWords(bag,2);
bag = removeEmptyDocuments(bag)

bag = 
  bagOfWords with properties:

          Counts: [28137×6974 double]
      Vocabulary: [1×6974 string]
        NumWords: 6974
    NumDocuments: 28137

Fit LDA Model

Fit an LDA model with 7 topics. For an example showing how to choose the number of topics, see Choose Number of Topics for LDA Model. To suppress verbose output, set 'Verbose' to 0.

numTopics = 7;
mdl = fitlda(bag,numTopics,'Verbose',0);

If you have a large dataset, then the stochastic approximate variational Bayes solver is usually better suited as it can fit a good model in fewer passes of the data. The default solver for fitlda (collapsed Gibbs sampling) can be more accurate at the cost of taking longer to run. To use stochastic approximate variational Bayes, set the 'Solver' option to 'savb'. For an example showing how to compare LDA solvers, see Compare LDA Solvers.

Visualize Topics Using Word Clouds

You can use word clouds to view the words with the highest probabilities in each topic. Visualize the first four topics using word clouds.

figure;
for topicIdx = 1:4
    subplot(2,2,topicIdx)
    wordcloud(mdl,topicIdx);
    title("Topic " + topicIdx)
end

View Mixtures of Topics in Documents

Use transform to transform the documents into vectors of topic probabilities.

newDocument = tokenizedDocument("A tree is downed outside Apple Hill Drive, Natick");
topicMixture = transform(mdl,newDocument);

figure
bar(topicMixture)
xlabel("Topic Index")
ylabel("Probability")
title("Document Topic Probabilities")

Visualize multiple topic mixtures using stacked bar charts. Visualize the topic mixtures of the first 5 input documents.

figure
topicMixtures = transform(mdl,documents(1:5));
barh(topicMixtures(1:5,:),'stacked')
xlim([0 1])
title("Topic Mixtures")
xlabel("Topic Probability")
ylabel("Document")
legend("Topic " + string(1:numTopics),'Location','northeastoutside')

Preprocessing Function

The function preprocessText, performs the following steps in order:

1. Tokenize the text using tokenizedDocument.

2. Lemmatize the words using normalizeWords.

3. Erase punctuation using erasePunctuation.

4. Remove a list of stop words (such as "and", "of", and "the") using removeStopWords.

5. Remove words with 2 or fewer characters using removeShortWords.

6. Remove words with 15 or more characters using removeLongWords.

function documents = preprocessText(textData)

% Tokenize the text.
documents = tokenizedDocument(textData);

% Lemmatize the words.
documents = addPartOfSpeechDetails(documents);
documents = normalizeWords(documents,'Style','lemma');

% Erase punctuation.
documents = erasePunctuation(documents);

% Remove a list of stop words.
documents = removeStopWords(documents);

% Remove words with 2 or fewer characters, and words with 15 or greater
% characters.
documents = removeShortWords(documents,2);
documents = removeLongWords(documents,15);

end