Project description

GPyM_TM

GPyM_TM is a Python package to perform topic modelling, either through the use of a Dirichlet multinomial mixture model, or a Poisson model. Each of the above models is available within the package in a separate class, namely GSDMM utilizes the Dirichlet multinomial mixture model, while GPM makes use of the Poisson model to perform the text clustering respectively. The package is also available on Pypi.

Preamble

The aim of topic modelling is to extract latent topics from large corpora. GSDMM [1] assumes each document belongs to a single topic, which is a suitable assumption for some short texts. Given an initial number of topics, K, this algorithm clusters documents and extracts the topical structures present within the corpus. If K is set to a high value, then the model will also automatically learn the number of clusters.

[1] Yin, J. and Wang, J., 2014, August. A Dirichlet multinomial mixture model-based approach for short text clustering. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 233-242).

Getting Started:

The package is available online for use within Python 3 enviroments.

The installation can be performed through the use of a standard 'pip' install command, as provided below:

pip install GPyM-TM

Prerequisites:

The package has several dependencies, namely:

numpy
random
math
pandas
re
nltk
gensim
scipy

GSDMM

Function and class description:

The class is named GSDMM, while the function itself is named DMM.

The function can take 6 possible arguments, two of which are required, and the remaining 4 being optional.

The required arguments are:

corpus - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed.
nTopics - the number of topics.

The optional requirements are:

alpha, beta - these are the distribution specific parameters.(The defaults for both of these parameters are 0.1.)
nTopWords - number of top words per a topic.(The default is 10.)
iters - number of Gibbs sampler iterations.(The default is 15.)

Output:

The function provides several components of output, namely:

psi - topic x word matrix.
theta - document x topic matrix.
topics - the top words per topic.
assignments - the topic numbers of selected topics only, as well as the final topic assignments.
Final k - the final number of selected topics.
coherence - the coherence score, which is a performance measure.
selected_theta
selected_psi

GPM

Function and class description:

The class is named GPM, while the function itself is named GPM.

The function can take 8 possible arguments, two of which are required, and the remaining 6 being optional.

The required arguments are:

corpus - text file, which has been cleaned and loaded into Python. That is, the text should all be lowercase, all punctuation and numbers should have also been removed.
nTopics - the number of topics.

The optional requirements are:

alpha, beta and gam - these are the distribution specific parameters.(The defaults for these parameters are alpha = 0.001, beta = 0.001 and gam = 0.1 respectively.)
nTopWords - number of top words per a topic.(The default is 10.)
iters - number of Gibbs sampler iterations.(The default is 15.)
N - this is a parameter used to normalize the document lengths, which is required for the Poisson model.

Output:

The function provides several components of output, namely:

psi - topic x word matrix.
theta - document x topic matrix.
topics - the top words per topic.
assignments - the topic numbers of selected topics only, as well as the final topic assignments.
Final k - the final number of selected topics.
coherence - the coherence score, which is a performance measure.
selected_theta
selected_psi