Topic Extractor (Parallel LDA)

Simple parallel threaded implementation of LDA, following Newman, Asuncion, Smyth and Welling, Distributed Algorithms for Topic Models JMLR (2009), with SparseLDA sampling scheme and data structure from Yao, Mimno and McCallum, Efficient Methods for Topic Model Inference on Streaming Document Collections, KDD (2009).

The node uses the "MALLET: A Machine Learning for Language Toolkit." topic modeling library. Note: The current version of MALLET contains a known multi-threading bug that can cause the node to fail with an ArrayIndexOutOfBoundsException. Should you encounter this issue, setting the number of threads to one should solve the problem.


Document column
The column that contains the pre-processed document.
The seed used for random number drawing.
No of topics
The number of topics to detect.
No of words per topic
The number of top words to extract per topic.
No of iterations
Number of iterations to perform (influences the runtime of the algorithm).
The alpha parameter defines the Dirichlet prior on the per-document topic distributions. It defines the prior weight of topic k in a document. The library uses the given alpha for all topics. Normally a number less than 1, e.g. 0.1, to prefer sparse topic distributions, i.e. few topics per document.
The beta parameter defines the prior on per-topic multinomial distribution over words. It defines the prior weight of word w in a topic. The library uses the given beta for all words. Normally a number much less than 1, e.g. 0.001, to strongly prefer sparse word distributions, i.e. few words per topic.
No of threads
Divides the input document collection into the specified number of threads and merges the calculated statistics afterwards.

Input Ports

Data table with the document collection to analyze. Each row contains one document.

Output Ports

The document collection with topic assignments and the probability for each document to belong to a certain topic
The topic models with the terms and their weight per topic
Table with statistics for each iteration


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