Documents by Theme Frame

Example

>>> from sklearn.decomposition import LatentDirichletAllocation
>>> lda = LatentDirichletAllocation(
...     n_components=10,
...     learning_decay=0.7,
...     learning_offset=50.0,
...     max_iter=10,
...     batch_size=128,
...     evaluate_every=-1,
...     perp_tol=0.1,
...     mean_change_tol=0.001,
...     max_doc_update_iter=100,
...     random_state=0,
... )
>>> from techminer2.packages.topic_modeling.user import DocumentsByThemeDataFrame
>>> df =(
...     DocumentsByThemeDataFrame()
...     #
...     # FIELD:
...     .with_field("raw_descriptors")
...     .having_terms_in_top(50)
...     .having_terms_ordered_by("OCC")
...     .having_term_occurrences_between(None, None)
...     .having_term_citations_between(None, None)
...     .having_terms_in(None)
...     #
...     # DECOMPOSITION:
...     .using_decomposition_algorithm(lda)
...     .using_top_terms_by_theme(5)
...     #
...     # TFIDF:
...     .using_binary_term_frequencies(False)
...     .using_row_normalization(None)
...     .using_idf_reweighting(False)
...     .using_idf_weights_smoothing(False)
...     .using_sublinear_tf_scaling(False)
...     #
...     # DATABASE:
...     .where_root_directory_is("examples/fintech/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> df.head() 
cluster                                                    0  ...         9
article                                                       ...
Alt R., 2018, ELECTRON MARK, V28, P235              0.033338  ...  0.699979
Anagnostopoulos I., 2018, J ECON BUS, V100, P7      0.947050  ...  0.005883
Anshari M., 2019, ENERGY PROCEDIA, V156, P234       0.016672  ...  0.016670
Arner D.W., 2017, NORTHWEST J INTL LAW BUS, V37...  0.006668  ...  0.006668
Belanche D., 2019, IND MANAGE DATA SYS, V119, P...  0.899982  ...  0.011113

[5 rows x 10 columns]