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In contefranz/OpTop: Optimal topic specification for latent dirichlet allocation models

OpTop 0.9.5

Major Changes

• word_proportions(): after careful testing, we decided to deprecate this function. The main reason is the lack of support for high-dimensional dfm inputs. Now all the functions that require word proportions take advantage of the class dfm from quanteda.

• sim_dfm(): we introduced a new function to easily simulate a document-feature-matrix from a LDA specification. This is useful for simulating corpora for testing.

Minor Changes

• Document matching between input LDAs and weighted_dfm has been improved. Now the check does not rely on a specific docvar anymore but rather on the internal document naming convention as defined in the corpus.

• Small tweaks in the computation speed for certain operations.

• Improved documentation.

Bug Fix

• If the optimal model is the last in the list of LDA objects passed to optimal_topic(), now all the other functions devoted to testing return NULL with a message. This is because there is nothing to test in terms of topic stability above the optimal model.

OpTop 0.9.4

Major Changes

• word_proportions() is much general now and can carry out complex preprocessing routines.
• optimal_topic() is much faster now due to matrix implementation. Now it checks for the presence of documents in both the corpus/dfm and the estimates by LDA().

Minor Changes

• The argument remove_documents in word_proportions() is now set to FALSE by default. This automatically triggers document check in optimal_topic().
• Preprocessing in word_proportions() is achieved by the new general argument ....
• word_proportion() can now work on a corpus and dfm objects as defined in quanteda.
• Improved documentation

OpTop 0.9.3

• Fixing tiny minor things

OpTop 0.9.1

• Just kidding! No more data here...we'll figure out something later on...

OpTop 0.9.0

Major Changes

• A set of pre-run LDA models are now available.

OpTop 0.8.0

Major Changes

• agg_document_stability() has been fully implemented. The function returns both the Aggregated Document Stability test and the F-test on informative and uninformative components.

Minor Changes

• General improvements in plots.

• Better documentation.

• Slighty faster functions.

OpTop 0.7.0

Minor Changes

• agg_topic_stability() can now compute smoothed tests and plot the results accordingly.

• Support for final convertion to a tibble table spreaded out to all functions,

• All eligible functions get better plots.

OpTop 0.6.0

Major Changes

• optimal_topic() gains the parameter q which allows to select the quantile of the cumulative probability of word weights to consider as relevant.

• optimal_topic() now finds the optimal number of topics either by significance levels or by forcing the algorithm to reach the global minimum. This is controlled by the new parameter alpha.

Minor Changes

• optimal_topic() drops both threshold and q_type.

• In optimal_topic(), convert now supports tibble structure.

OpTop 0.5.0

Major Changes

• Function agg_topic_stability() has been widely improved.

• All functions which return a test now gain the new argument do_plot. This plot the test statistic as a function of the number of topics.

• The argument test has been removed from topic_stability() which now returns only the aggregate statistic.

• The argument compute_res has been finally removed from topic_stability().

• topic_stability() now returns either a data.frame or a data.table with the LDA specifiction associated to each statistic (i.e. column topic).

Other Changes

• Improved documentation for some functions.

OpTop 0.4.0

New Functions

Since we have two more functions, I feel like this deserves a jump in package version.

• topic_match(): detect and extract informative and uninformative components.

• agg_topic_stability(): implements Test 4 from the methodological paper [Lewis and Grossetti (2019)].

Other Changes

• Improved documentation for some functions.

OpTop 0.2.0

New Functions

• get_topic_models(): handy function to immediately get the list of topic models the user wants to process from a specified environment;

• topic_stability(): implements Tests 2 and 3 from the methodological paper [Lewis and Grossetti (2019)].

Other Changes

• Formal declaration of LDA_VEM objects as functions input.

• All the functions now have more detailed and better documentations.

• Added Continuous Integration with Travis CI and AppVeyor.

Bug Fixes

• Choice of quantile algorithms is now fully supported.

OpTop 0.1.0

First version!

New Functions

• word_proportions(): computes word proportions from a corpus object created by quanteda [Benoit et al. (2018)];

• optimal_topic(): implements Test 1 from the methodological paper [Lewis and Grossetti (2019)].

contefranz/OpTop documentation built on Feb. 14, 2022, 7:04 p.m.