Example 1 of running basic topic model (LDA)

This example shows how to run the LDA Gibbs sampler on a small dataset to extract a set of topics and shows the most likely words per topic. It also writes the results to a file

Choose the dataset

dataset = 1; % 1 = psych review abstracts 2 = NIPS papers

if (dataset == 1)
    % load the psych review data in bag of words format
    load 'bagofwords_psychreview';
    % Load the psych review vocabulary
    load 'words_psychreview'
elseif (dataset == 2)
    % load the nips dataset
    load 'bagofwords_nips';
    % load the nips vocabulary
    load 'words_nips'
end

Set the number of topics

T=50;

Set the hyperparameters

BETA=0.01;
ALPHA=50/T;

The number of iterations

N = 100;

The random seed

SEED = 3;

What output to show (0=no output; 1=iterations; 2=all output)

OUTPUT = 1;

This function might need a few minutes to finish

tic
[ WP,DP,Z ] = GibbsSamplerLDA( WS , DS , T , N , ALPHA , BETA , SEED , OUTPUT );
toc

	Iteration 0 of 100
	Iteration 10 of 100
	Iteration 20 of 100
	Iteration 30 of 100
	Iteration 40 of 100
	Iteration 50 of 100
	Iteration 60 of 100
	Iteration 70 of 100
	Iteration 80 of 100
	Iteration 90 of 100
Elapsed time is 14.615118 seconds.

Just in case, save the resulting information from this sample

if (dataset==1)
    save 'ldasingle_psychreview' WP DP Z ALPHA BETA SEED N;
end

if (dataset==2)
    save 'ldasingle_nips' WP DP Z ALPHA BETA SEED N;
end

Put the most 7 likely words per topic in cell structure S

[S] = WriteTopics( WP , BETA , WO , 7 , 0.7 );

fprintf( '\n\nMost likely words in the first ten topics:\n' );


Most likely words in the first ten topics:

Show the most likely words in the first ten topics

S( 1:10 )

ans = 
    'memory recognition retrieval recall items item list'
    'representations representation bias single scale known relation'
    'information processing action levels automatic components controlled'
    'theory theories predictions account explain formation esteem'
    'visual attention brain mechanism selection color attentional'
    'speech system target motor masking neural relative'
    'network semantic ability test lexical predictions normal'
    'states related emotional positive primary arousal motivation'
    'control conditioning responses conditions avoidance procedures suggested'
    'stimulus effects shown concepts trial free generalization'

Write the topics to a text file

WriteTopics( WP , BETA , WO , 10 , 0.7 , 4 , 'topics.txt' );

fprintf( '\n\nInspect the file ''topics.txt'' for a text-based summary of the topics\n' );


Inspect the file 'topics.txt' for a text-based summary of the topics